Multi-sensory, assistive wearable technology, and method of providing sensory relief using same

ABSTRACT

A system and method for providing sensory relief from distractibility, inattention, anxiety, fatigue, and/or sensory issues to a user in need. The user can be autistic/neurodiverse, or neurotypical. The system can be configured to connect to a datastore storing one or more sensory thresholds specific to a user of a wearable device of the system, the sensory thresholds selected from auditory, visual or physiological sensory thresholds; record, using one or more sensors of the wearable device, a sensory input stimulus to the user; compare the sensory input stimulus with the sensory thresholds to determine an intervention to be provided to the user, the intervention configured to provide the user relief from distractibility, inattention, anxiety, fatigue, or sensory issues; and provide the intervention to the user, the intervention comprising filtering, in real-time, an audio signal presented to the user or an optical signal presented to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional application No.63/229,963, titled “MULTI-SENSORY, ASSISTIVE WEARABLE TECHNOLOGY, ANDMETHOD OF PROVIDING SENSORY RELIEF USING SAME” filed Aug. 5, 2021, andU.S. provisional application No. 63/238,490, titled “MULTI-SENSORY,ASSISTIVE WEARABLE TECHNOLOGY, AND METHOD OF PROVIDING SENSORY RELIEFUSING SAME” filed Aug. 30, 2021. The aforementioned applications areincorporated herein by reference in their entirety.

FIELD

The present application describes an assistive wearable technology thatfilters sensory distractions, increases attentional focus, and lessensanxiety for autistic (neurodiverse) individuals, and methods ofproviding sensory relief using the assistive wearable technology.

BACKGROUND

In this specification where a document, act or item of knowledge isreferred to or discussed, this reference or discussion is not anadmission that the document, act or item of knowledge or any combinationthereof was at the priority date, publicly available, known to thepublic, part of common general knowledge, or otherwise constitutes priorart under the applicable statutory provisions; or is known to berelevant to an attempt to solve any problem with which thisspecification is concerned.

A significantly high percentage (about 90%) of autistic adults reportthat sensory issues cause significant barriers at school and/or work.(Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2007).Describing the Sensory Abnormalities of Children and Adults with Autism.Journal of Autism and Developmental Disorders, 37(5), 894— 910).Additionally, 87% of autistic employees feel environmental adjustmentswould make critical differences to their performance. (Maltz, S. (2019).Autistica Action Briefing: Employment-Harper G, Smith E, Heasman B,Remington A, Girdler S, Appleton V J, Cameron C, Fell C). These numbersmake a compelling case for addressing the sensory issues that affect anautistic adult's ability to function successfully. Environmental factorsare also known to trigger persistent sensory and cognitive challenges(e.g., sensory overload) leading to mental health challenges. Mentalhealth is the number one autistic priority and primary barrier toschooling/employment (Cusack, J., & Sterry, R. (2019, December).Autistica's top 10 research priorities), and it contributessubstantially to autism's societal expenditures, which in the UK exceeds£27.5 billion per annum—surpassing cancer, heart, stroke, and lungdiseases combined. (Knapp, M., Romeo, R., & Beecham, J. (2009). Economiccost of autism in the UK. Autism, 13(3), 317-336); (London School ofEconomics (2014). Autism is the most costly medical condition in theUK).

SUMMARY

This application addresses the above-described challenges, by providinga wearable technology that offers ground-breaking opportunities to: (i)monitor environments and adjust user-experiences; (ii) lessensensory-load and enable greater participation; and (iii) improve mentalhealth with efficacious interventions. The wearable technology describedherein increases attentional focus, reduces sensory distraction, andimproves quality-of-life/lessens anxiety and fatigue.

It should be understood that the various individual aspects and featuresof the present invention described herein can be combined with any oneor more individual aspect or feature, in any number, to form embodimentsof the present invention that are specifically contemplated andencompassed by the present invention.

One embodiment of the application is directed to a system, comprising: awearable device comprising one or more sensors; one or more processors;and one or more non-transitory computer-readable media having executableinstructions stored thereon that, when executed by the one or moreprocessors, cause the system to perform operations comprising:connecting to a datastore that stores one or more sensory thresholdsspecific to a user of the wearable device, the one or more sensorythresholds selected from auditory, visual or physiological sensorythresholds; recording, using the one or more sensors, a sensory inputstimulus to the user; comparing the sensory input stimulus with the oneor more sensory thresholds specific to the user to determine anintervention to be provided to the user, the intervention configured toprovide the user relief from distractibility, inattention, anxiety,fatigue, or sensory issues; and providing the intervention to the user,the intervention comprising filtering, in real-time, an audio signalpresented to the user or an optical signal presented to the user. Thephysiological sensory thresholds can bephysiological/psychophysiological sensory thresholds.

In some implementations, the operations further comprise:communicatively coupling the system to an Internet of Things (IoT)device, the sensory input stimulus generated at least in part due tosound emitted by a speaker of the IoT device or light emitted by a lightemitting device of the IoT device; and providing the intervention to theuser, comprises: controlling the IoT device to filter, in real-time, theaudio signal or the optical signal.

In some implementations, the IoT device comprises the light emittingdevice; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.

In some implementations, the IoT device comprises the speaker;controlling the IoT device to filter, in real-time, the audio signal orthe optical signal, comprises controlling the IoT device to filter, inreal-time, the audio signal; and filtering the audio signal adjusts afrequency of sound output by the speaker.

In some implementations, the wearable device further comprises a boneconduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.

In some implementations, the wearable device further comprises a headmounted display (HMD) that presents the optical signal to the user, theHMD worn by the user; and providing the intervention to the user furthercomprises filtering, in real-time, the optical signal by modifying areal-time image of the real-world environment presented to the user viathe HMD.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: determining, basedon the same sensor data recorded by the one or more sensors, to filterthe audio signal and to filter the optical signal.

In some implementations, the wearable device further comprises a HMDthat presents the optical signal to the user, the HMD worn by the user;and providing the intervention to the user includes filtering, inreal-time, the optical signal by modifying a real-time image of thereal-world environment presented to the user via the HMD.

In some implementations, modifying the real-time image comprisesinserting a virtual object into the real-time image or modifying theappearance of an object of the real-world environment in the real-timeimage.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: inputting thesensory input stimulus and the one or more user-specific sensorythresholds into a trained model to automatically determine, based on anoutput of the trained model, a visual intervention to be provided to theuser.

In some implementations, the one or more sensors comprise multiplesensors of different types, the multiple sensors comprising: an auditorysensor, a galvanic skin sensor, a pupillary sensor, a body temperaturesensor, a head sway sensor, or an inertial movement unit; recording thesensory input stimulus to the user comprises recording a first sensoryinput stimulus from a first sensor of the multiple sensors, and a secondsensory input stimulus from a second sensor of the multiple sensors; andinputting the sensory input stimulus into the trained model comprisesinputting the first sensory input stimulus and the second sensory inputstimulus into the trained model.

In some implementations, the visual intervention comprises: presentingan alert to the user of a visually distracting object; and after it isdetermined that the user does not sufficiently respond to the alertwithin a period of time, filtering, in real-time, the optical signalpresented to the user.

In some implementations, the visual intervention comprises: filtering,in real-time, the optical signal to hide a visually distracting objectwithout providing a prior alert to the user that the visuallydistracting object is present.

In some implementations, the operations further comprise determining theone or more sensory thresholds specific to the user and one or moreinterventions specific to the user by: presenting multiple selectabletemplates to the user, each of the templates providing an indication ofwhether the user is visually sensitive, sonically sensitive, orinteroceptively sensitive, and each of the templates associated withcorresponding one or more sensory thresholds and one or moreinterventions; and receiving data corresponding to input by the userselecting one of the templates.

In some implementations, determining the one or more sensory thresholdsspecific to the user and the one or more interventions specific to theuser further comprises: receiving additional data corresponding toadditional user input selecting preferences, the preferences comprisingaudio preferences, visual preferences, physiological preferences, alertpreferences, guidance preferences, or intervention preferences; and inresponse to receiving the additional data, modifying the one or morethresholds and the one or more interventions of the selected template toderive the one or more sensory thresholds specific to the user and theone or more interventions specific to the user. In some implementations,the physiological preferences are psychophysiological preferences.

In some implementations, comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: inputting thesensory input stimulus and the one or more user-specific sensorythresholds into a trained model to automatically determine, based on anoutput of the trained model, the intervention to be provided to theuser.

In some implementations, the user is neurodiverse. In someimplementations, the user can be autistic.

In some implementations, the intervention further comprises an alertintervention; and with the alert intervention, a response time for theuser increases by at least 3% and accuracy increases by at least about26% from baseline for errors of commission, the errors of commissionbeing a measure of a failure of the user to inhibit a response whenprompted by a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user increases by at least about 20% and accuracy increases by atleast about 10% from baseline for errors of commission, the errors ofcommission being a measure of a failure of the user to inhibit aresponse when prompted by a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user increases by at least about 2% and accuracy increases by atleast about 30% from baseline for errors of omission, the errors ofomission being a measure of a failure of the user to take appropriateaction when a prompt is not received from a feedback device.

In some implementations, with the intervention to filter, a responsetime for the user increases by at least about 10% from baseline forerrors of omission, the errors of omission being a measure of a failureof the user to take appropriate action when a prompt is not receivedfrom a feedback device.

In some implementations, with the intervention to filter, a responsetime for the user is at least about 15% faster than would be a responsetime for a neurotypical user using the system for errors of omission,the errors of omission being a measure of a failure of the user to takeappropriate action when a prompt is not received from a feedback device.

In some implementations, the intervention further comprises a guidanceintervention; and with the guidance intervention, a response time forthe user is at least about 20% faster and accuracy is about 8% higherthan would be a response time and accuracy of a neurotypical user usingthe system for errors of commission, the errors of commission being ameasure of a failure of the user to inhibit a response when prompted bya feedback device.

In some implementations, the intervention further comprises an alertintervention; and with the alert intervention, accuracy for the user isat least about 25% higher than would be an accuracy of a neurotypicaluser using the system for errors of commission, the errors of commissionbeing a measure of a failure of the user to inhibit a response whenprompted by a feedback device.

One embodiment of the application is directed to a method, comprising:connecting a wearable device system to a datastore that stores one ormore sensory thresholds specific to a user of a wearable device of thewearable device system, the one or more sensory thresholds selected fromauditory, visual or physiological sensory thresholds; recording, usingone or more sensors of the wearable device, a sensory input stimulus tothe user; comparing, using the wearable device system, the sensory inputstimulus with the one or more sensory thresholds specific to the user todetermine an intervention to be provided to the user, the interventionconfigured to provide the user relief from distractibility, inattention,anxiety, fatigue, or sensory issues; and providing, using the wearabledevice system, the intervention to the user, the intervention comprisingfiltering, in real-time, an audio signal presented to the user or anoptical signal presented to the user. In some implementations, thephysiological preferences are psychophysiological preferences.

In some implementations, the method further comprises communicativelycoupling the wearable device system to an IoT device; providing theintervention to the user comprises controlling the IoT device to filter,in real-time, the audio signal or the optical signal; and the sensoryinput stimulus generated at least in part due to sound emitted by aspeaker of the IoT device or light emitted by a light emitting device ofthe IoT device.

In some implementations, the IoT device comprises the light emittingdevice; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.

In some implementations, the IoT device comprises the speaker;controlling the IoT device to filter, in real-time, the audio signal orthe optical signal, comprises controlling the IoT device to filter, inreal-time, the audio signal; and filtering the audio signal adjusts afrequency of sound output by the speaker.

In some implementations, the wearable device further comprises a boneconduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.

One embodiment of this application is directed to a system for providingsensory relief from distractibility, inattention, anxiety, fatigue,sensory issues, or combinations thereof, to a user in need thereof, thesystem comprising: (i.) a wearable device; (ii) a database of one ormore user-specific sensory thresholds selected from auditory, visual,and physiological sensory thresholds, one or more user-specific sensoryresolutions selected from auditory, visual and physiological sensoryresolutions, or combinations thereof; (iii) an activation means forconnecting the wearable device and the database; (iv) one or moresensors for recording a sensory input stimulus to the user; (v) acomparing means for comparing the sensory input stimulus recorded by theone or more sensors with the database of one or more user-specificsensory thresholds to obtain a sensory resolution for the user; (vi) oneor more feedback devices for transmitting the sensory resolution to theuser; and (vii) a user-specific intervention means for providing reliefto the user from the distractibility, inattention, anxiety, fatigue,sensory issues, or combinations thereof. The user-specific interventionmeans is selected from an alert intervention, a filter intervention, aguidance intervention, or a combination thereof, and the user can be aneurodiverse user or a neurotypical user. In a preferred embodiment, theneurodiverse user can be an autistic user. In some implementations, thephysiological sensory thresholds are psychophysiological sensorythresholds, and the physiological sensory resolutions arepsychophysiological sensory resolutions.

In some implementations, the wearable device is an eyeglass framecomprising the one or more sensors and the one or more feedback devices.

In some implementations, the one or more sensors are selected from oneor more infrared sensors, one or more auditory sensors, one or moregalvanic skin sensors, one or more inertial movement units, orcombinations thereof.

In some implementations, the one or more feedback devices are selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the system further comprises a wireless orwired hearing device.

In some implementations, the sensory input stimulus is selected from anecological auditory input, an ecological visual input, a egocentricphysiological/psychophysiological input, or combinations thereof.

In some implementations, the sensory input stimulus is measured byevaluating one or more parameters selected from eye tracking,pupillometry, auditory cues, interoceptive awareness, physical movement,variations in body temperature or ambient temperatures, pulse rate,respiration, or combinations thereof.

In some implementations, the sensory resolution is provided by one ormore alerts selected from a visual alert, an auditory alert, aphysiological/psychophysiological alert, a verbal alert, or combinationsthereof.

In some implementations, the activation means is a power switch locatedon the wearable device.

In some implementations, the power switch is located at a left side ofthe wearable device.

In some implementations, the power switch is located at a right side ofthe wearable device.

In some implementations, the power switch is a recessed power switch.

In some implementations, the database is stored in a storage device.

In some implementations, the storage device is selected from a fixed ormovable computer system, a portable wireless device, a smartphone, atablet, or combinations thereof.

In some implementations, with an alert intervention, a response time forautistic users increases by at least about 3% and accuracy increases byat least about 26% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, a response time forneurotypical users increases by at least about 18% and accuracyincreases by at least about 2.0% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 20% and accuracyincreases by at least about 10% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with guidance intervention, a response time forautistic users increases by at least about 2% and accuracy increases byat least about 30% from baseline for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice.

In some implementations, with a filter intervention, a response time forautistic users increases by at least about 10% from baseline for errorsof omission, wherein the errors of omission is a measure of the user'sfailure to take appropriate action when a prompt is not received fromthe feedback device.

In some implementations, with a filter intervention, a response time forautistic users is at least about 15% faster than neurotypical users forerrors of omission, wherein the errors of omission are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users is at least about 20% faster and accuracy is about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, accuracy forautistic users is at least about 25% higher than neurotypical users forerrors of commission, wherein the errors of commission are a measure ofthe user's failure to inhibit a response when prompted by the feedbackdevice.

In one embodiment, a method of providing sensory relief fromdistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, comprises: creating adatabase of one or more user-specific sensory thresholds selected fromauditory, visual and physiological/psychophysiological sensorythresholds, one or more user-specific sensory resolutions selected fromauditory, visual and physiological/psychophysiological sensoryresolution, or combinations thereof; attaching a wearable device to theuser, wherein the wearable device comprises one or more sensors and oneor more feedback devices; activating and connecting the wearable deviceto the database; recording a sensory input stimulus to the user via theone or more sensors; comparing the sensory input stimulus with thedatabase of one or more user-specific sensory thresholds; selecting anappropriate user-specific sensory resolution from the database;delivering the user-specific sensory resolution to the user via the oneor more feedback devices; and providing a user-specific intervention(a/k/a digital mediation) to provide relief to the user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, wherein the user-specific intervention is selectedfrom an alert intervention, a filter intervention, a guidanceintervention, or a combination thereof, and wherein the user is anautistic user, a neurotypical user, or a neurodiverse user.

In some implementations, the one or more sensors are selected from oneor more infrared sensors, one or more microphones, one or more galvanicskin sensors, one or more inertial movement units, or combinationsthereof.

In some implementations, the one or more feedback devices is selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the sensory input stimulus is selected from anauditory input, a visual input, a physiological/psychophysiologicalinput or combinations thereof.

In some implementations, the sensory input stimulus is measured by oneor more parameters selected from eye tracking, pupillometry, auditorycues, interoceptive awareness, physical movement, variations in body orambient temperatures, pulse rate, respiration, or combinations thereof.

In some implementations, the user-specific sensory resolution isprovided by one or more alerts selected from a visual alert, an auditoryalert, a physiological/psychophysiological alert, a verbal alert orcombinations thereof.

In some implementations, the activation and connection of the wearabledevice to the database is through a power switch located on the wearabledevice.

In some implementations, the power switch is located at a left side ofthe wearable device or a right side of the wearable device

In some implementations, the power switch is a recessed power switch.

In some implementations, the wearable device is an eyeglass frame.

In some implementations, the database is stored in a storage device.

In some implementations, the storage device is selected from a fixed ormovable computer system, a portable wireless device, a smartphone, atablet, or combinations thereof.

In some implementations, with an alert intervention, a response time forautistic users increases by at least about 3% and accuracy increases byat least about 26% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, a response time forneurotypical users increases by at least about 18% and accuracyincreases by at least about 2.0% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 20% and accuracyincreases by at least about 10% from baseline for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users increases by at least about 2% and accuracy increasesby at least about 30% from baseline for errors of omission, wherein theerrors of omission are a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice.

In some implementations, with a filter intervention, a response time forautistic users increases by at least about 10% from baseline for errorsof omission, wherein the errors of omission are a measure of the user'sfailure to take appropriate action when a prompt is not received fromthe feedback device.

In some implementations, with a filter intervention, a response time forautistic users is at least about 15% faster than neurotypical users forerrors of omission, wherein the errors of omission are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device.

In some implementations, with a guidance intervention, a response timefor autistic users is at least about 20% faster and accuracy is about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device.

In some implementations, with an alert intervention, accuracy forautistic users is at least about 25% higher than neurotypical users forerrors of commission, wherein the errors of commission are a measure ofthe user's failure to inhibit a response when prompted by the feedbackdevice.

In one embodiment, a wearable device comprises one or more sensors andone or more feedback devices, wherein a combination of the one or moresensors and the one or more feedback devices provides sensory relieffrom distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user/wearer in need thereof.

In some implementations, the wearable device is an eyeglass frame.

In some implementations, the one or more sensors are connected to theeyeglass frame.

In some implementations, the one or more feedback devices are connectedto the eyeglass frame.

In some implementations, the eyeglass frame comprises a rim, twoearpieces and hinges connecting the earpieces to the rim.

In some implementations, the one or more sensors are selected from thegroup consisting of one or more infrared sensors, one or more auditorytransducers, one or more galvanic skin sensors, one or more inertialmovement units, or combinations thereof.

In some implementations, the infrared sensor is surface-mounted on aninner side of the wearable device.

In some implementations, the infrared sensor is arranged to be incidenton a right eye, a left eye or both eyes of a user.

In some implementations, the auditory transducer is a subminiaturemicrophone.

In some implementations, the subminiature microphone is surface-mountedon an outer side of the wearable device.

In some implementations, the wearable device comprises at least twoauditory transducers, wherein a first auditory transducer is arranged atan angle of about 110° to a second auditory transducer.

In some implementations, the galvanic skin sensor is surface-mounted onan inner side of the wearable device, and wherein the galvanic skinsensor is in direct contact with skin of a user.

In some implementations, the inertial movement unit isinternally-mounted on an inner-side of the wearable device.

In some implementations, the one or more feedback devices are selectedfrom one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

In some implementations, the haptic drive is internally mounted on aninner side of the wearable device.

In some implementations, the haptic drive is internally mounted on aninner side of the wearable device and behind the inertial movement unit.

In some implementations, the haptic drive provides a vibration patternin response to a sensory input stimulus selected from eye tracking,pupillometry, auditory cues, interoceptive awareness, physical movement,variations in body or ambient temperature, pulse rate, respiration, orcombinations thereof.

In some implementations, the stereophonic bone conduction transducer issurface-mounted on an inner side of the wearable device, and thestereophonic bone conduction transducer is in direct contact with auser's skull.

In some implementations, the stereophonic bone conduction transducerprovides an auditory tone, a pre-recorded auditory guidance, real-timefiltering, or combinations thereof, in response to a sensory inputstimulus selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body orambient temperature, pulse rate, respiration, or combinations thereof.

In some implementations, the wearable device further comprises anoptional wireless or wired hearing device.

In some implementations, the wearable device further comprises anintervention means to providing relief to a user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, the intervention means selected from an alertintervention, a filter intervention, a guidance intervention, or acombination thereof.

In some implementations, the wearable device further comprises a powerswitch. The power switch can be located at a left side of the wearabledevice or a right side of the wearable device. The power switch can be arecessed power switch.

In one embodiment, a non-transitory computer-readable medium hasexecutable instructions stored thereon that, when executed by aprocessor, cause a wearable device to perform operations comprising:connecting the wearable device to a datastore that stores one or moresensory thresholds and one or more sensory resolutions specific to auser, the one or more sensory thresholds selected from auditory, visualor physiological/psychophysiological sensory thresholds, and the one ormore sensory resolutions selected from auditory, visual, orphysiological/psychophysiological sensory resolutions; recording, viaone or more sensors, a sensory input stimulus to the user; comparing thesensory input stimulus recorded by the one or more sensors with one ormore sensory thresholds to obtain a sensory resolution for the user; andtransmitting the sensory resolution to the user.

In some implementations, the operations further comprise:communicatively coupling to an IoT device providing the sensory inputstimulus to the user; and transmitting the sensory resolution to theuser, comprises: after communicatively coupling to the IoT device,controlling the IoT device to transmit the sensory resolution.

In some implementations, the IoT device comprises a networked lightingdevice; and controlling the IoT device to transmit the sensoryresolution, comprises: controlling a brightness or color output of thenetworked lighting device.

In some implementations, the IoT device comprises a networked speaker;and controlling the IoT device to transmit the sensory resolution,comprises: controlling a volume, an equalization setting, or a channelbalance of the networked speaker.

In some implementations, comparing the sensory input stimulus recordedby the one or more sensors with the one or more user-specific sensorythresholds to obtain the sensory resolution for the user, comprises:inputting the sensory input stimulus and the one or more user-specificsensory thresholds into a trained model to automatically determine thesensory resolution for the user.

In some implementations, the operations further comprise determining theone or more sensory thresholds and the one or more sensory resolutionsby: presenting multiple selectable templates to the user, each of thetemplates providing an indication of whether the user is visuallysensitive, sonically sensitive, or interoceptively sensitive, and eachof the templates associated with corresponding one or more thresholdsand one or more sensory resolutions; and receiving data corresponding toinput by the user selecting one of the templates.

In some implementations, determining the one or more user-specificsensory thresholds and the one or more user-specific sensory resolutionsfurther comprises: receiving additional data corresponding to additionaluser input selecting preferences, the preferences comprising audiopreferences, visual preferences, physiological/psychophysiologicalpreferences, alert preferences, guidance preferences, or interventionpreferences; and in response to receiving the additional data, modifyingthe one or more thresholds and one or more sensory resolutions of theselected template to derive the one or more user-specific sensorythresholds and the one or more user-specific sensory resolutions.

Other features and aspects of the disclosed method will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresin accordance with embodiments of the disclosure. The summary is notintended to limit the scope of the claimed disclosure, which is definedsolely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The figures are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosure.

FIG. 1 is a schematic representation of a wearable device, in accordancewith some implementations of the disclosure.

FIG. 2 is a graphical representation of sensitivities across threemodalities—visual, aural and anxiety—as observed in Pre-Trial BatteryExamination (PTBE), as described herein.

FIG. 3 is a graphical representation of interest in a wearable deviceamong autism spectrum condition (ASC) participants in PTBE.

FIG. 4 is a flowchart of a standard study protocol of SustainedAttention to Response Task (SART) testing.

FIG. 5 is a flowchart of a standard Wizard of Oz (Wizard of Oz) studyprotocol.

FIG. 6 is a flowchart of the SART/WoZ study protocol, in accordance withsome implementations of the disclosure.

FIG. 7 is a graphical representation of recruitment scores of studyparticipants for the wearable device studies.

FIGS. 8A to 8C are graphical representations of the Errors of Commission(EOC) of the full cohort of participants in the SART/WoZ study describedherein. FIG. 8A shows the EOC from baseline to baseline. FIG. 8B showsthe EOC intervention effect. FIG. 8C shows the lasting effect of EOC.

FIGS. 9A to 9C are graphical representations of EOC as it relates toResponse Time (RT) of the full cohort of participants in the SART/WoZstudy described herein. FIG. 9A shows the EOC vs RT from startingbaseline to final baseline. FIG. 9B shows the EOC vs RT interventioneffect. FIG. 9C shows the lasting effect of EOC vs RT.

FIGS. 10A to 10C are graphical representations of EOC grouped by studyparticipants.

FIGS. 11A to 11C are graphical representations of EOC vs RT grouped bystudy participants.

FIGS. 12A to 12C are graphical representations of the Errors of Omission(EOO) of the full cohort of participants in the SART/WoZ study describedherein. FIG. 12A shows the EOO from starting baseline to final baseline.FIG. 12B shows the EOO intervention effect. FIG. 12C shows the lastingeffect of EOO.

FIGS. 13A to 13C are graphical representations of EOO as it relates toRT of the full cohort of participants in the SART/WoZ study describedherein. FIG. 13A shows the EOO vs RT from starting baseline to finalbaseline. FIG. 13B shows the EOO vs RT intervention effect. FIG. 13Cshows the lasting effect of EOO vs RT.

FIGS. 14A to 14C are graphical representations of EOO grouped by studyparticipants.

FIGS. 15A to 15C are graphical representations of EOO vs RT grouped bystudy participants.

FIG. 16 is a block diagram of components of a wearable device, inaccordance with some implementations of the disclosure.

FIG. 17 is a block diagram of additional microprocessor details (ARMprocessor) of a wearable device, in accordance with some implementationsof the disclosure.

FIG. 18 is a flowchart of the various components of the study variables,in accordance with some implementations of the disclosure.

FIG. 19 depicts a wearable device system including a wearable device incommunication with a mobile device and a datastore, in accordance withsome implementations of the disclosure.

FIG. 20 shows an operational flow diagram depicting an example methodfor initializing and iteratively updating one or more sensory thresholdsand one or more interventions associated with a specific user, inaccordance with some implementations of the disclosure.

FIG. 21 depicts a wearable device system including a wearable device incommunication with a mobile device that controls an IoT device with aspeaker, in accordance with some implementations of the disclosure.

FIG. 22 depicts a wearable device system including a wearable device incommunication with a mobile device that controls an IoT device with alight emitting device, in accordance with some implementations of thedisclosure.

FIG. 23 depicts an example wearable device that can be utilized toprovide visual interventions, in accordance with some implementations ofthe disclosure.

FIG. 24A depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including hapticalerts, tone alerts guidance, and an eraser effect, in accordance withsome implementations of the disclosure.

FIG. 24B depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including a text alert,a blur effect, and a cover-up effect, in accordance with someimplementations of the disclosure.

FIG. 24C depicts interventions that can be delivered using a real-timeoptical enhancement algorithm, the interventions including colorbalance, a contrast effect, and an enhancement effect, in accordancewith some implementations of the disclosure.

FIG. 25 depicts one particular example of a workflow that uses areal-time optical enhancement algorithm to provide interventions, inreal-time, in a scenario where there is a distracting visual source, inaccordance with some implementations of the disclosure.

The figures are not exhaustive and do not limit the disclosure to theprecise form disclosed.

DETAILED DESCRIPTION

Further aspects, features and advantages of this invention will becomeapparent from the detailed description which follows.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Additionally, the use of “or” is intended to include“and/or”, unless the context clearly indicates otherwise.

As used herein, “about” is a term of approximation and is intended toinclude minor variations in the literally stated amounts, as would beunderstood by those skilled in the art. Such variations include, forexample, standard deviations associated with conventional measurementtechniques or specific measurement techniques described herein. All ofthe values characterized by the above-described modifier “about,” arealso intended to include the exact numerical values disclosed herein.Moreover, all ranges include the upper and lower limits.

Any apparatus, device or product described herein is intended toencompass apparatus, device or products which consist of, consistessentially of, as well as comprise, the various constituents/componentsidentified herein, unless explicitly indicated to the contrary.

As used herein, the recitation of a numerical range for a variable isintended to convey that the variable can be equal to any value(s) withinthat range, as well as any and all sub-ranges encompassed by the broaderrange. Thus, the variable can be equal to any integer value or valueswithin the numerical range, including the end-points of the range. As anexample, a variable which is described as having values between 0 and10, can be 0, 4, 2-6, 2.75, 3.19-4.47, etc.

In the specification and claims, the singular forms include pluralreferents unless the context clearly dictates otherwise. As used herein,unless specifically indicated otherwise, the word “or” is used in the“inclusive” sense of “and/or” and not the “exclusive” sense of“either/or.”

Unless indicated otherwise, each of the individual features orembodiments of the present specification are combinable with any otherindividual features or embodiments that are described herein, withoutlimitation. Such combinations are specifically contemplated as beingwithin the scope of the present invention, regardless of whether theyare explicitly described as a combination herein.

Technical and scientific terms used herein have the meaning commonlyunderstood by one of skill in the art to which the present descriptionpertains, unless otherwise defined. Reference is made herein to variousmethodologies and materials known to those of skill in the art.

As used herein, the term “alert intervention” is intended to include asfollows: in the event of an ecological and/or physiological (e.g.,psychophysiological) threshold's activation that corresponds to awearer's preferences, a signal is delivered to: (i) a haptic driver thatprovides a gentle, tactile vibration pattern to convey information tothe wearer that focus, anxiety, fatigue or related characteristicsrequire their attention; and/or (ii) a bone conduction transducer thatdelivers an auditory/sonic message (e.g., pre-recorded text-to-speech,beep tone, etc.) reinforcing the haptic with an aural intervention andset of instructions.

As used herein, the term “filter intervention” is intended to include asfollows: in the event of an ecological and/or physiological (e.g.,psychophysiological) threshold's activation that corresponds to awearer's preferences and requires auditory filtering, digital audiosignal processing delivers real-time and low-latency audio signals thatinclude corrected amplitude (compression, expansion), frequency(dynamic, shelving, low/hi-cut, and parametric equalization), spatialrealignment (reposition, stereo to mono) and/or phase correction (timedelay, comb filtering, linear phase alignment). In an embodiment, thefilter invention can be delivered to a bone conduction transducer. Inother embodiments, the filter invention can be delivered to optionalwireless or wired hearing devices, including but not limited to earbuds,earphones, headphones, and the like.

As used herein, the term “guidance intervention” includes anintervention similar to an alert intervention, where the guidance can beprovided by way of step-by-step instructions for re-alignment of focus,head sway, pupillary activity, pulse, temperature, respiration, anxiety,and fatigue coaching. These pre-recorded, text-to-speech audio streamscan be delivered to bone conduction systems, which provide step-by-stepinstructional intervention both privately and unobtrusively.

As used herein, the term “combination intervention” includes as follows:an intervention that can be selected by the wearer, which can be acombination of alert, filter and guidance interventions, and which areprovided depending upon the triggering mechanism. For example, onlysonic disturbances can be addressed through filter intervention, whileall other issues (attentional-focus, anxiety, fatigue, and the like) canbe intervened through haptic, text-to-speech alerts, long-formstep-by-step guidance, and the like.

As used herein, the terms “user” and “wearer” are used interchangeably.

As used herein, the term “errors of commission” are a measure of theuser's failure to inhibit a response when prompted by the feedbackdevice

As used herein, the term “errors of omission” are a measure of theuser's failure to take appropriate action when a prompt is not receivedfrom the feedback device

As used herein, the term “response time” is intended to include the timetaken by a participant to respond to a sensory cue and/or an alert,filter and/or guidance intervention. Response Time may also beinterchangeably referred to as Reaction Time and is defined as theamount of time between when a participant perceives a sensory cue andwhen the participant responds to said sensory cue. Response Time orReaction Time is the ability to detect, process, and respond to astimulus.

As used herein to refer to processing such as, for example, anyprocessing that can include filtering of an audio signal and/or anoptical signal that is presented to a user, the term “real-time” isintended to refer to processing and/or filtering the signal with aminimal latency after the original audio signal and/or optical signaloccurs. For example, the latency can be a non-zero value of about 500milliseconds(ms) or less, about 250 ms or less, about 200 ms or less,about 150 ms or less, about 100 ms or less, about 90 ms or less, about80 ms or less, about 70 ms or less, about 60 ms or less, about 50 ms orless, about 40 ms or less, about 30 ms or less, about 20 ms or less, orabout 10 ms or less, ranges and/or combinations thereof and the like.The minimum latency can be subject to system and hardware and softwarelimitations, including communication protocol latency, digital signalprocessing latency, electrical signal processing latency, combinationsthereof and the like. In some instances, real-time filtering of an audiosignal and/or an optical signal can be perceived by a user as beingimmediate, instantaneous or nearly immediate and/or instantaneous.

Autism Spectrum Condition (ASC) is a life-long diagnosis, which has asubset of features including hyper-, seeking- and/or hypo-reactivity tosensory inputs or unusual interests. These qualities are evident acrossenvironmental (e.g., response to specific sounds, visual fascinationwith lights or movements) and physiological/psychophysiological domains(e.g., anxiety, respiration or euthermia). Scholars report that ninety(90%) of autistic adults experience sensory issues causing significantbarriers at school/work (Leekam et al., 2007). Individuals with ASCoften exhibit persistent deficits in social communication andinteraction across multiple contexts. An additional hallmark includesrestricted, repetitive patterns of behavior and interests (RRBI).Importantly, RRBIs include hyper-, seeking- and/or hypo-reactivity tosensory input along with attainably unusual interests in sensory aspectsof the environment and physiological/psychophysiological responses tovisuals, textures, smells, touch, and sounds. As the diagnosis of ASCpopulations increases exponentially over time, an ever-expanding socialpolicy chasm proliferates, whereby an autistic individual's smoothtransition into the fabric of daily life is often compromised. Expertsidentify this as a gap stemming from either: (i) stuntedpublic/government support for neurodiverse individuals; ii) tensionsbetween the autism community and society; and iii) limited support forlater-life educational/vocational pathways. The negative effects ofpolicy-related factors are a consequence resulting in societal coststhat have a potential to become still more significant and possiblyirremediable.

This application provides various interventions to alter, redirectand/or attenuate disruptive stimuli. Namely, described herein aresystems, devices and methods to determine whether distractions exist,which can be exacerbated at school and at work, and provideinterventions to compensate for such distractions, thereby lesseninganxiety for neurotypical and neurodiverse individuals, and providingsensory relief. This application aspires to help individuals learn,adapt, and internalize how best to respond to encroaching ecologicalstimuli and resulting physiological/psychophysiological responses.Wearables, as described herein, may, through repetitive processesobserved and experienced by users, pave the way for a call and responseprocess that may eventually transfer directly from a machine or systemto the person, thus embedding guidance for similarly reoccurring/futurescenarios. An autistic individual, for example, might watch, experience,and learn precisely how an Artificial Intelligence/Cognitive Enhancementsystem detects, filters and coaches herself when confronted with anundesirable sensory stimulus.

One embodiment is directed to a system for providing sensory relief fromdistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, the system comprising:(i.) a wearable device; (ii) a database of one or more user-specificsensory thresholds selected from auditory, visual andphysiological/psychophysiological sensory thresholds, one or moreuser-specific sensory resolutions selected from auditory, visual andphysiological/psychophysiological sensory resolutions, or combinationsthereof (iii) an activation means for connecting the wearable device andthe database; (iv) one or more sensors for recording a sensory inputstimulus to the user; (v) a comparing means for comparing the sensoryinput stimulus recorded by the one or more sensors with the database ofone or more user-specific sensory thresholds to obtain a sensoryresolution for the user; (vi) one or more feedback devices fortransmitting the sensory resolution to the user; and (vii) auser-specific intervention means for providing relief to the user fromthe distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof. The user-specific intervention means can beselected from an alert intervention, a filter intervention, a guidanceintervention, or a combination thereof. The user can be an autisticuser, a neurodiverse user or a neurotypical user.

Another embodiment is directed to a method of providing sensory relieffrom distractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, to a user in need thereof, the method comprising:(i) creating a database of one or more user-specific sensory thresholdsselected from auditory, visual and physiological/psychophysiologicalsensory thresholds, one or more user-specific sensory resolutionsselected from auditory, visual and physiological/psychophysiologicalsensory resolution, or combinations thereof (ii) attaching a wearabledevice to the user, wherein the wearable device comprises one or moresensors and one or more feedback devices; (iii) activating andconnecting the wearable device to the database; (iv) recording a sensoryinput stimulus to the user via the one or more sensors; (v) comparingthe sensory input stimulus with the database of one or moreuser-specific sensory thresholds; (vi) selecting an appropriateuser-specific sensory resolution from the database; (vi) delivering theuser-specific sensory resolution to the user via the one or morefeedback devices; and (vii) providing a user-specific intervention toprovide relief to the user from the distractibility, inattention,anxiety, fatigue, sensory issues, or combinations thereof.

Another embodiment is directed to a wearable device comprising one ormore sensors and one or more feedback devices. According to furtherembodiments, the wearable device can be an eyeglass frame. One or moresensors and/or one or more feedback devices can be connected to theeyeglass frame. The eyeglass frame may comprise a rim, two earpieces andhinges connecting the earpieces to the rim. In alternate embodiments,the wearable device may include jewelry, smart clothing, andaccessories, including but not limited to rings, sensor woven fabrics,wristbands, watches, pins, hearing aid, assistive devices, medicaldevices, virtual, augmented, and mixed reality (VR/AR/MR) headsets, andthe like. The wearable device may have the ability to coordinate withmobile and/or network devices for alert, filter, and guidanceinterventions, and may include sensors and feedback devices in variouscombinations.

According to further embodiments, the one or more sensors can beselected from one or more infrared sensors, one or more auditorysensors, one or more galvanic skin sensors, one or more inertialmovement units, or combinations thereof. The infrared sensor can besurface-mounted on an inner side of the wearable device. The infraredsensor can be arranged to be incident on a right eye, a left eye or botheyes of a user.

According to further embodiments, the one or more feedback devices canbe selected from one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof.

According to further embodiments, the wearable device may furthercomprise a wireless or wired hearing device.

According to further embodiments, the sensory input stimulus can beselected from an auditory input, a visual input, aphysiological/psychophysiological input or combinations thereof. Thesensory input stimulus can be measured by evaluating one or moreparameters selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body orambient temperature, pulse rate, respiration, or combinations thereof.The sensory resolution can be provided by one or more alerts selectedfrom a visual alert, an auditory alert, aphysiological/psychophysiological alert, a verbal alert or combinationsthereof.

According to further embodiments, the activation means can be a powerswitch located on the wearable device. The power switch can be locatedat a left side of the wearable device and/or at a right side of thewearable device. The power switch can be a recessed power switch. Inanother embodiment, power may be supplied when in stand-by mode from auser interface component, including but not limited to mobile phones,laptops, tablets, desktop computers, and the like, and any userinterface known in the field can be used without limitation. In anotherembodiment, the activation means may include a power switch or powersource that can be activated remotely (i.e., when not in proximity of auser).

In another embodiment, the activation means may be triggered by thewearable's accelerometer, pupillary and head sway sensors, and the like.For example, when an accelerometer is selected as an activation means,the accelerometer senses when the wearer (and wearable) is idle. In thisinstance, the unit can be in a low-power or power-off mode, and when thewearable is engaged (e.g., the wearable is lifted from a surface, moveor agitated), such engagement is recognized by the accelerometer, whichswitches the wearable into a power-on mode. Similarly, for example, whenpupillary or head sway sensors are used as an activation means, thepower management system includes the ability to place the unit into abattery conservation mode (e.g., low-power mode). If, for example, awearer was to shut their eyes whilst resting with a wearable “in place”,the sensors would react to a novel movement and immediately return thesystem into a powered-on state when/if the user was to eventually arisefrom a period of rest, and the like.

In another embodiment, an activation means may include a power-onactivity programmed from a biopotential analogue front end (AFE), whichincludes galvanic skin sensor response applications includingperspiration, heart rate, blood pressure, temperature, and the like, allof which can trigger an activation of the wearable device.

In another embodiment, the wearable device's activation can be fullyaccessed by any type of network device/protocol because of its IoTconnectivity, which enables communication, activation, and the like, ofthe wearable device.

According to further embodiments, a database can be stored in a storagedevice. The storage device can be selected from a fixed or movablecomputer system, a portable wireless device, a smartphone, a tablet, orcombinations thereof. In an alternate embodiment, the database can bestored locally on or in the wearable device. In another alternateembodiment, the databased can be stored remotely, including but notlimited to cloud-based systems, secured datacenters behind DMZ, and thelike, and the database can be in encrypted and decrypted communicationwith the secured wearable device and its data.

Based on any of the exemplary embodiments described herein, a responsetime for autistic users increases by at least about 0.5% to about 5%,about 1% to about 4.5%, about 1.5% to about 4%, about 2% to about 3.5%,and preferably about 3% after alert intervention and accuracy increasesby at least about 10% to about 50%, about 15% to about 40%, about 20% toabout 30%, and preferably about 26% from baseline for errors ofcommission, wherein the errors of commission are a measure of the user'sfailure to inhibit a response when prompted by the feedback device. Anumerical value within these ranges can be equal to any integer value orvalues within any of these ranges, including the end-points of theseranges.

Based on the exemplary embodiments described herein, a response time forneurotypical users increases by at least about 0.5% to about 50%, about5% to about 40%, about 10% to about 30%, about 15% to about 20%, andpreferably about 18% after alert intervention and accuracy increases byat least about 0.01% to about 5%, about 0.05% to about 4%, about 1% toabout 3%, and preferably about 2.0% from baseline for errors ofcommission, wherein the errors of commission are a measure of the user'sfailure to inhibit a response when prompted by the feedback device. Anumerical value within these ranges can be equal to any integer value orvalues within any of these ranges, including the end-points of theseranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.5% to about 50%, about 1%to about 40%, about 10% to about 30%, and preferably about 20% afterguidance intervention and accuracy increases by at least about 0.5% toabout 30%, about 1.0% to about 20%, about 5% to about 15%, andpreferably about 10% from baseline for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device. A numerical value withinthese ranges can be equal to any integer value or values within any ofthese ranges, including the end-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.01% to about 5%, about0.05% to about 4%, about 1% to about 3%, and preferably about 2% afterguidance intervention and accuracy increases by at least about 10% toabout 50%, about 15% to about 45%, about 20% to about 40%, andpreferably about 30% from baseline for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users increases by at least about 0.5% to about 30%, about 1.0%to about 20%, about 5% to about 15%, and preferably about 10% frombaseline after filter intervention for errors of omission, wherein theerrors of omission is a measure of the user's failure to takeappropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users is at least about 0.5% to about 30%, about 1.0% to about25%, about 5% to about 20%, and preferably about 15% faster thanneurotypical users after filter intervention for errors of omission,wherein the errors of omission are a measure of the user's failure totake appropriate action when a prompt is not received from the feedbackdevice. A numerical value within these ranges can be equal to anyinteger value or values within any of these ranges, including theend-points of these ranges.

Based on the exemplary embodiments described herein, a response time forautistic users is at least about 0.5% to about 50%, about 1% to about40%, about 10% to about 30%, and preferably about 20% faster afterguidance intervention and accuracy is at least about 0.5% to about 30%,about 1.0% to about 20%, about 5% to about 15%, and preferably about 8%higher than neurotypical users for errors of commission, wherein theerrors of commission are a measure of the user's failure to inhibit aresponse when prompted by the feedback device. A numerical value withinthese ranges can be equal to any integer value or values within any ofthese ranges, including the end-points of these ranges.

Based on the exemplary embodiments described herein, accuracy forautistic users is at least about 0.5% to about 50%, about 1% to about4%, about 10% to about 30%, and preferably about 25% higher thanneurotypical users after alert intervention for errors of commission,wherein the errors of commission are a measure of the user's failure toinhibit a response when prompted by the feedback device. A numericalvalue within these ranges can be equal to any integer value or valueswithin any of these ranges, including the end-points of these ranges.

According to further embodiments, an auditory transducer can be asubminiature microphone. The subminiature microphone may preferably besurface-mounted on an outer side of the wearable device.

According to further embodiments, a wearable device may include at leasttwo auditory transducers, and the arrangement of the first and secondauditory transducers can be one that is known in the art, including butnot limited to the first and second auditory transducers being arrangedat an angle ranging from about 45° to about 135°, about 55° to about130°, about 65° to about 125°, about 75° to about 120°, about 85° toabout 120°, about 95° to about 115°, about 100°, about 110°, and thelike. The numerical value of any specific angle within these ranges canbe equal to any integer value or values within any of these ranges,including the end-points of these ranges can be.

According to further embodiments, a galvanic skin sensor can besurface-mounted on an inner side of the wearable device, and thegalvanic skin sensor can be in direct contact with skin of a user. Theinner side of the wearable device can be a side facing the skin orsubstantially facing the skin.

According to further embodiments, an inertial movement unit maypreferably be internally-mounted on an inner-side of the wearabledevice.

According to further embodiments, the one or more feedback devices canbe selected from one or more haptic drivers, one or more bone conductiontransducers, or combinations thereof. The haptic drive can be internallymounted on an inner side of the wearable device. The haptic drive can beinternally mounted on an inner side of the wearable device and behindthe inertial movement unit. The haptic drive provides a vibrationpattern in response to a sensory input stimulus selected from eyetracking, pupillometry, auditory cues, interoceptive awareness, physicalmovement, variations in body or ambient temperature, pulse rate,respiration, or combinations thereof. In another exemplary embodiment,the feedback device may also include a heads-up visual component, orother feedback devices that provide pupillary projection, distractingvisual blurring, removal, squelching, recoloring, or combinationsthereof.

According to further embodiments, the stereophonic bone conductiontransducer can be surface-mounted on an inner side of the wearabledevice, and the stereophonic bone conduction transducer can be in directcontact with a user's skull. The stereophonic bone conduction transducerprovides an auditory tone, a pre-recorded auditory guidance, real-timefiltering, or combinations thereof, in response to a sensory inputstimulus selected from eye tracking, pupillometry, auditory cues,interoceptive awareness, physical movement, variations in body andambient temperature, pulse rate, respiration, or combinations thereof.

According to further embodiments, the wearable device may furtherinclude an intervention means to providing relief to a user from thedistractibility, inattention, anxiety, fatigue, sensory issues, orcombinations thereof, wherein the intervention means is selected from analert intervention, a filter intervention, a guidance intervention, or acombination thereof. In exemplary embodiments, the intervention meansand the feedback means can be the same or different.

Various possible intervention means available to the user and deliveredby the wearable device are illustrated in the block diagram of FIG. 16 .As illustrated in FIG. 16 , following sensor(s) data stream delivery andmicroprocessor 312 comparison between ecological/environmental andphysiological/psychophysiological thresholds to real-time data, thoseevents deemed subject for interventional processing can be delivered toone of two discrete (or simultaneous) components: a haptic driver 313 ora bone conduction transducer 314. Pending a wearer's previously definedpreferences (stored in the microprocessor), one of four interventionalstrategies can be invoked: alert, filter, guidance, or combination.

Alert intervention: In the event of an ecological and/orphysiological/psychophysiological threshold's activation thatcorresponds to a wearer's preferences, a signal is delivered to: (i) thehaptic driver that provides a gentle, tactile vibration pattern toconvey information to the wearer that focus, anxiety, fatigue or relatedcharacteristics require their attention; and/or (ii) the bone conductiontransducer(s) that deliver an auditory/sonic message (e.g., pre-recordedtext-to-speech, beep tone, etc.) reinforcing the haptic with an auralintervention and set of instructions.

Filter intervention: In the event of an ecological and/orphysiological/psychophysiological threshold's activation thatcorresponds to a wearer's preferences and requires auditory filtering,digital audio signal processing delivers real-time and low-latency audiosignals that include corrected amplitude (compression, expansion),frequency (dynamic, shelving, low/hi-cut, and parametric equalisation),spatial realignment (reposition, stereo to mono) and/or phase correction(time delay, comb filtering, linear phase alignment). Though typicallydelivered to bone conduction transducers, these can be delivered tooptional wireless or wired hearing devices, including but not limited toearbuds, earphones, headphones, and the like.

Guidance intervention: Similar to alert intervention, the guidance byway of step-by-step instructions for re-alignment in focus, head sway,pupillary activity, anxiety, and fatigue coaching is provided to awearer. These pre-recorded, text-to-speech audio streams are deliveredto the bone conduction systems, which provide step-by-step instructionalintervention both privately and unobtrusively.

Combination intervention: Selectable by the wearer, a combination ofalert, filter and guidance interventions are provided depending upon thetriggering mechanism. For example, only sonic disturbances are addressedthrough filter intervention, while all other issues (attentional-focus,anxiety, etc.) can be intervened through haptic, text-to-speech alertsand long-form step-by-step guidance.

According to further embodiments, the wearable device may furtherinclude a power switch. The power switch can be located at a left sideof the wearable device and/or a right side of the wearable device. Thepower switch can be a recessed power switch.

In an embodiment, the wearable device may have a structure illustratedin FIG. 1 . As illustrated in FIG. 1 , the wearable device 10 can be inthe form of an eyeglass frame including a rim 109, left and rightearpieces, each having a temple portion 106 and temple tip 108 andscrews 103 and hinges 104 connecting the earpieces to the rim 109. Theframe may further include lenses 101, a nose pad 102, end pieces 107,and a bridge 105 connecting left- and right-sides of the frame. Thewearable device may have one or more sensors connected to the frame,including infrared pupillometry sensors 204, galvanic skin sensors 205,inertial movement units 206, wireless transceiver and A/D multiplexers208, microphones 201, and the like. The wearable device 10 may alsoinclude one or more feedback devices connected to the frame, includinghaptic drivers 203, bone conduction transducers 202, and the like. Thewearable device 10 may further include an optional wireless or wiredhearing device 209, and a power switch (not shown) and/or a rechargeablepower source 207.

Although the wearable device is depicted as an eyeglass frame in FIG. 1, it should be appreciated that the wearable device can be implementedusing a different type of head mount such as a visor or helmet. Otherexemplary embodiments of the wearable device can include, but are notlimited to, wrist worn devices, bone conduction devices, and the like,and any wearable device known in the field and adaptable to the methoddescribed herein can be used, and any of which may work in conjunctionwith a user interface described herein. In some cases, the wearabledevice can be implemented as a combination of devices (e.g., wearableeyeglasses, ring, wrist-worn, clothing/textile, and watch).

In some implementations, the wearable device can be communicativelycoupled to a mobile device (e.g., smartphone and/or other smart device)that controls operations of, works in concert with, and/or provides auser interface for change settings of the wearable device. For example,FIG. 19 depicts a wearable device system including a wearable device 10in communication with a mobile device 20, and a datastore 30. In thisexample system, the wearable device 10 communicates with mobile device20 over a wireless communication network. The wireless communicationnetwork can be any suitable network that enables communications betweenthe devices. The wireless communication network can be an ad-hoc networksuch as a WiFi network, a Bluetooth network, and/or a network using someother communication protocol. In some implementations, the wearabledevice 10 can be tethered to mobile device 20. In some implementations,the mobile device 20 processes sensor data collected by one or moresensors of wearable device 10. For example, the mobile device 20 candetermine, based on the processed sensor data, one or more interventionsto be applied using the wearable device 10 and/or some other device. Thedetermination can be based on one or more sensory thresholds 31 specificto a user wearing the wearable device 10. The interventions that areapplied can be based on one or more user-specific sensory resolutions 32specific to the user. Although the datastore storing thresholds 31 and32 is illustrated in this example as being separate from wearable device10 and mobile device 20, in other implementations the datastore 30 canbe incorporated within wearable device 10 and/or mobile device 20.

Prior to use, the user can initiate personalization of the wearabledevice by identifying individual sound, visual andphysiological/psychophysiological thresholds using software integratedin the wearable device. Personalization can identify unique sensory,attentional-focus and anxiety/fatigue producing cues that a user findsdistracting particularly in educational, employment, social, and typicaldaily activities, and can be derived from the Participant PublicInformation (PPI) study described herein. The user-specific thresholdsare used to customize subsequent alerts, filters, and guidanceexperienced by the user when wearing the wearable device. Uponcompletion of the personalization process, the thresholds aretransmitted to the wearable device. The personalization thresholds maybe updated over time (e.g., periodically or dynamically) as the useradapts to stimuli or is presented with new stimuli.

In some implementations, the device may be configured via a mobileapplication (app), web-based application or other web-based interface(e.g., website). During the personalization process, the user can bepresented with a graphical user interface or other user interface viathe wearable device or via a smartphone or other device communicativelycoupled to the wearable device. For example, the wearable device orother device can include a processor that executes instructions thatcause the device to present (e.g., display) selectable controls orchoices to the user that are used to refine a set of thresholds, alerts,filters, and/or guidance in discrete or combined formats. In someimplementations, the personalization process can be conducted by thewearer of the device, a healthcare provider, or a caretaker of the user.For example, the personalization process can be conducted by running anapplication instance on the wearable device or other device andreceiving data corresponding to input from the wearer, health provider,or caretaker making selections (e.g., telemetry/biotelemetry). In somecases, different user interfaces and options can be presented dependingon whether personalization is conducted by the wearer, healthcareprovider, or caretaker.

In some implementations of the personalization process, a datastoreassociated with the wearable device may pre-store initializationtemplates that correspond to a particular set of thresholds (e.g.,sound, visual, and/or physiological/psychophysiological) and/or alerts,filters, and/or guidance. For example, templates corresponding topredominantly sonically sensitive wearers, predominantly visuallysensitive wearers, predominantly interoceptive sensitive wears,combination wearers, and the like can be preconfigured and stored by thesystem. During device initialization, the wearer can select one of thetemplates (e.g., the user is predominantly visually sensitive), and theconfigured parameters (e.g., thresholds, alerts, filters, and/orguidance) for the selected template can be further customized inresponse to additional user input. The additional user input can includeresponses to questions, or a selection of preferences as furtherdiscussed below.

In some implementations, each of the templates can be associated with atrained model that given a set of inputs (e.g., sensor readings from thewearable device, user thresholds, etc.) generates one or more outputs(e.g., alerts, filters, guidance, sonic feedback, visual feedback,haptic feedback) experienced by the user. The model can be trained andtested with anonymized historical data associated with users to predictappropriate outputs given sensory inputs and thresholds. Supervisedlearning, semi-supervised learning, or unsupervised learning can beutilized to build the model. During a personalization process, furtherdiscussed below, parameters of the model (e.g., weights of inputvariables) can be adjusted depending on the user's sensitivities and/orselections.

In some implementations of the personalization process, the user can bepresented with selectable preferences and/or answers to questions. Forexample, the personalization process may present the user withselectable choices relating to demographics (e.g., gender, age,education level, handedness, etc.) and sensitivities (e.g., audiopreferences, visual preferences, physiological/psychophysiologicalpreferences, alert preferences, guidance preferences, interventionranking preferences, and the like). Depending on the user's selections,a particular set of thresholds (e.g., sound, visual, and/orphysiological/psychophysiological), alerts, filters, and/or guidance maybe customized for the user and stored. For instance, based on the user'sselection of audio preferences, the system can be configured to performdigital signal processing of audio signals before audio is played to theuser to adjust the energy of different frequency ranges (e.g., bass,mid-range, treble, etc.) within the audible frequency band (e.g., 20 Hzto 20,000 Hz), the audio channels that emit sound, or othercharacteristics of audio. During configuration, the user can specify apreference for filtering (e.g., enhancing, removing, or otherwisealtering) low-range sounds, mid-range sounds, high-range sounds, softsounds, loud sounds, reverberant sounds, surround sounds, etc. Asanother example, a user can specify a preference for receiving alerts ofsounds having particular sonic characteristics (e.g., alert for loud,echoing, and/or surround sounds before they occur). As a furtherexample, a user can prefer that guided sounds have particularcharacteristics (e.g., soft-spoken words, gentle sounds) when the userbecomes anxious, unfocused, or sensitive.

In some implementations, the user's selected preferences and/or answersduring personalization can be used to build a model that given a set ofinputs (e.g., sensor readings from the wearable device, user thresholds,etc.) generates one or more outputs (e.g., alerts, filters, guidance,sonic feedback, visual feedback, haptic feedback) experienced by theuser. For example, a website or mobile app configurator accessed via auser login can generate one or more tolerance scores based on the user'sanswers to questions pertaining to visual, auditory, orphysiological/psychophysiological stimuli. The one or more tolerancescores can be used to initialize the model. The model can also beinitialized, modified and/or monitored by a specialist, healthcareprovider, or caretaker.

During personalization, a user can rank the types of interventions. Forexample, a user can rank and/or specify a preferred type of alert (e.g.,beep, haptic, voice, or some combination thereof), a preferred audiofilter (e.g., volume (compression, limiting), equalization (tone, EQ),noise reduction, imaging (panning, phase), reverberation (echo), imaging(panning, phase), or some combination thereof), a preferred type ofguidance (e.g., encouragement), and the like.

In some implementations, one or more sensors of the wearable device canbe calibrated during initialization of the device. A user can bepresented with an interface for calibrating sensors and/or adjustingsensor parameters. For example, the user can specify whether all or onlysome sensors are active and/or gather data, adjust sensor sensitivity,or adjust a sensor threshold (e.g., brightness for an optical sensor,loudness for an audio sensor) and what order of implementation they aredesired (e.g., alerts first, followed by guidance, followed by filters,etc.).

In some implementations, after an initial set of user-specificthresholds and alerts, filters, and guidance are set up for a user,validation of the configuration can be conducted by presenting the userwith external stimuli, and providing alerts, filters, and/or guidance inaccordance with the user-configured thresholds. Depending on the user'sresponse, additional configuration can be conducted. This validationprocess can also adjust sensor settings such as sensor sensitivity.

In implementations where a trained model is used to provide alerts,filters, and/or guidance, the model can be retrained over time based oncollected environmental and/or physiological/psychophysiological data.To save on computational resources and/or device battery life,retraining can be performed at night and/or when the system is not inuse.

FIG. 20 shows an operational flow diagram depicting an example method400 for initializing and iteratively updating one or more sensorythresholds and one or more interventions associated with a specificuser. In some implementations, method 400 can be implemented by one ormore processors (e.g., one or more processors of wearable device 10and/or mobile device 20) of a wearable device system executinginstructions stored in one or more computer readable media (e.g., one ormore computer readable media of wearable device 10 and/or mobile device20). Operation 401 includes presenting multiple selectable templates tothe user, the multiple templates corresponding to one or more sensorythresholds and one or more interventions. The multiple selectabletemplates can be presented via a GUI (e.g., using wearable device 10and/or mobile device 20). Operation 402 includes receiving datacorresponding to input by the user selecting one of the templates. Afteruser selection of one of the templates, the one or more sensorythresholds and one or more interventions associated with the templatecan be associated with the user. For example, the one or more sensorythresholds and one or more interventions can be stored in a datastore 30including an identification and/or user profile corresponding to theuser. As described above, operations 401-402 can be performed duringand/or after an initialization process.

Operation 403 includes receiving data corresponding to user inputselecting preferences. The preferences can comprise audio preferences,visual preferences, physiological/psychophysiological preferences, alertpreferences, guidance preferences, and/or intervention preferences.Operation 404 includes in response to receiving additional datacorresponding to additional user input selecting preferences, modifyingthe one or more sensory thresholds and the one or more interventionsassociated with the user. For example, the datastore thresholds andinterventions can be updated. As depicted, operations 403-404 caniterate over time as the user desires to further define thethresholds/interventions and/or as the user develops new preferences.

Operation 405 includes collecting sensor data and environmental datawhile the user wears the wearable device. Operation 406 includes inresponse to collecting the sensor data and/or environmental data whilethe user wears the wearable device, modifying the one or more sensorythresholds and the one or more interventions associated with the user.As depicted, operations 405-406 can iterate over time as the userutilizes the wearable device system to provide sensory relief. Thefrequency with which the one or more sensory thresholds and the one ormore interventions are updated in response to newly-collected data canbe configurable, system-defined, and/or user-defined. For example,updates can depend on the amount of data that is collected and/or theamount of time that has passed. In some implementations, operations405-406 can be skipped. For example, the user can disable updating thethresholds and/or interventions based on actual use of the wearabledevice.

In some implementations, the wearable device can be configured tocommunicate with and/or control IoT devices that present stimuli. Forexample, based on configured thresholds for a user, the wearable devicecan control the operation of smart devices such as networked hubs,networked lighting devices, networked outlets, alarm systems, networkedthermostats, networked sound systems, networked display systems,networked appliances, and other networked devices associated with theuser. For instance, the audio output (e.g., loudness and balance) of anetworked sound system and/or display output (e.g., brightness,contrast, and color balance) of networked display system can be alteredto meet individual sound or visual thresholds. To synchronizecommunication and operation between the wearable devices and IoTdevices, the devices can be linked to an account of the user, which canbe configured via an application running on a smartphone (e.g., nativehome control application) or other device (e.g., mobile device 20). Insome instances, behavior or one or more scenes for an IoT device can bepreconfigured based on the thresholds associated with the user. Thebehavior or scenes can be activated when the wearable device detectsthat it is in the presence (e.g., same room) of the IoT device.

By way of illustration, FIG. 21 depicts a wearable device systemincluding a wearable device 10 in communication with a mobile device 20that controls an IoT device 40 with a speaker 41. As another example,FIG. 22 depicts a wearable device system including a wearable device 10in communication with a mobile device 20 that controls an IoT device 50with a light emitting device 51. During operation, wearable device 10can use one or more sensors to collect a sensory input stimulus. Thissensory input stimulus can be transmitted to a mobile device 20 thatcompares the sensory input stimulus with one or more sensory thresholdsspecific to the user (e.g., thresholds 31) to determine an interventionto be provided to the user, to provide the user relief fromdistractibility, inattention, anxiety, fatigue, and/or sensory issues.

In the example of FIG. 21 , the sensory input stimulus can be generatedat least in part due to sound emitted by the speaker 41 of the IoTdevice 40. For example, the user can generate aphysiological/psychophysiological response to music and/or other soundsbeing played at a certain frequency and/or range of frequencies byspeaker 41. In that scenario, the intervention can include the mobiledevice 20 controlling IoT device 40 to filter, in the frequency domain,an audio signal such that sound output by speaker 41 plays in afrequency that does not induce the samephysiological/psychophysiological response in the user.

In the example of FIG. 22 , the sensory input stimulus can be generatedat least in part due to light emitted by the light emitting device 51 ofthe IoT device 50. For example, the user can experience discomfort whenthe output light is too bright or too cool (e.g., >4000K) in colortemperature. This discomfort can be measured using the sensory inputstimulus collected by the one or more sensors of the wearable device 10.In that scenario, the intervention can include the mobile device 20controlling IoT device 50 to filter an optical signal of light device 51to lower a brightness and/or color temperature of light output by thelighting device 51.

Although the foregoing examples depict the mobile device 20 ascommunicating with and controlling IoT devices 40, 50, it should beappreciated that these functions can instead be performed by thewearable device 10.

The wearable device can include various user interface components,including but not limited to mobile phones, laptops, tablets, desktopcomputers, and the like, and any user interface known in the field canbe used. In some implementations, the wearable device can besynchronized with a smartphone. For example, the wearable device can beconfigured to accept calls, adjust call volume, present notificationsounds or vibrations, present ringtones, etc. The wearable device can begranted access to user contacts, text messages or other instantmessages, etc. In some instances, the intensity of sounds or vibrations,or the pattern of sounds or vibrations, presented via mobile integrationcan depend on configured thresholds of the user. The initialconfiguration and personalization of the wearable device can beconducted via an application installed on a smartphone or other device.

The wearable device can include one or more network interfaces (e.g.,WiFi, Bluetooth, cellular, etc.) for communicating with other networkeddevices and/or connecting to the Internet. For example, a WiFi interfacecan enable the wearable device to select and communicatively couple to alocal network, which can permit communication with IoT devices.Bluetooth can enable pairing between the wearable device and asmartphone or other device.

The wearable device can include or communicatively couple to one or moredatastores (e.g., memories or other storage device) that are accessedduring its operation. Storage can be local, over a network, and/or overthe cloud. Storage can maintain a record of user preferences, userperformance, trained models, and other data or instructions required tooperate the device.

In an exemplary embodiment, a wearable device is operated as describedherein. The wearable device can remain in passive mode, i.e.,non-operating mode, before it is worn by a user. This can optimizebattery life.

Once in active mode, i.e., when the wearable device is in an operatingmode, the wearable device detects and responds to one or more sensorycues selected from a myriad of sensory cues received and detected by oneor more sensors located on the wearable device. Such sensory cues caninclude environmental and physiological/psychophysiological signals, andthe like. The wearable device also provides additional and appropriateresolution in response to the sensory cues via alerts, filters, andguidance to the user whenever personalized thresholds for the use areexceeded. Thresholds and interventions can be iteratively set, adjusted,muted, and otherwise cancelled at any time and throughout the use of thewearable device by the user by returning to the computer/application.

Various types of sensory cues can be received and detected by thewearable device, including visual, auditory, andphysiological/psychophysiological cues, but are not limited thereto. Inan exemplary embodiment, visual distractions can be detected via eyetracking and pupillometry monitored by in infrared sensor that can besurface mounted on an inner side of the wearable device, for example, atan intersection of frame rim/right hinge temple and aimed at an eye ofthe user, for e.g., the right eye or the left eye or both. In anexemplary embodiment, auditory distractions and audiometric thresholdscan be monitored by subminiature and wired electret microphones that canbe surface mounted on an outer side of the end pieces, near theintersection of the frame front and temples. In an exemplary embodiment,physiological/psychophysiological distractions, interoceptive thresholdsand user head sway can be monitored by a galvanic skin sensor that issurface-mounted on an inner side of the left earpiece and in directcontact with the skin just above the user's neckline and/or an inertialmovement unit that is internally mounted on an inner side of thewearable device, and can be located behind an ear piece. The variousdetection components described herein are merely exemplary, and anysuitable components can be used.

Various types of resolutions (interventions or digital mediations) canbe provided to the user in response to the sensory cues received by thewearable device. The resolutions may include visual, auditory andphysiological/psychophysiological resolutions, but are not limitedthereto. In an exemplary embodiment, the visual resolutions can bedelivered through a haptic driver that can be internally mounted on aninner side of an ear piece and behind an inertial movement unitintersection of frame rim/right hinge temple. Visual resolutions can beprovided via unique vibrations associated with optical distractions whena pupillary or inertial/head sway threshold is detected. In anotherexemplary embodiment, the visual alerts can be delivered by astereophonic bone conduction that can be surface mounted on an innerside of wearable device, for example, at both temples midway between thehinges and temple tips and coming into direct contact with the user'sleft and right skull in front of each ear and provides either a beeptone and/or pre-recorded spoken guidance in the event a pupillary orinertial/head sway threshold is detected. In another exemplaryembodiment, auditory resolutions can be delivered to the user through asingle, haptic driver by providing uniquely coded vibrational alerts inthe event a sonic threshold is detected and/or through a bone conductiontransducer that provides both beep tone, pre-recorded spoken guidanceand/or real-time filtering using digital signal processing (DSP) forthose distracting, environmental audiometric events (e.g., compression,equalization, noise reduction, spatial panning, limiting, phaseadjustment, and gating) when a sonic threshold(s) is/are detected andcan be processed according to user's personalization settings. Forexample, in an exemplary embodiment, real-time digital audio streamsrecorded by microphones connected to the wearable device provide themicroprocessor with audio data that undergo system manipulation toachieve a predetermined goal. As described, the DSP produces feedback inthe form of altered audio signals (the filtered intervention) thatameliorates volume (amplitude, compression, noise reduction), tonal(equalization), directional (spatial, etc.). As another example,guidance may include one or more tonal alerts retrieved from adatastore.

In an exemplary embodiment, the device can be configured to boostcertain audible frequencies depending on the user's age or hearing. Forexample, the device can boost low, mid, and/or high frequenciesdepending on the user's age and/or hearing profile. In some cases, thedevice can execute instructions to provide a hearing test to generatethe hearing profile. A control can be provided to enable or disablesound boosting.

In an exemplary embodiment, physiological/psychophysiologicalresolutions can be delivered to the user through a haptic drivermentioned above and by providing uniquely coded vibrational alerts inthe event a physiological/psychophysiological, anxiety, fatigue or otherinteroceptive thresholds are detected and/or through a bone conductiontransducer, which provides both beep tone, and/or pre-recorded spokenguidance for similar threshold alert and guidance. The variousresolution components described herein are merely exemplary, and anysuitable components can be used.

In an exemplary embodiment, the wearable device may also include aninternally mounted central processing unit, that may further includesubminiature printed circuit boards combined with a self-containedconnected and rechargeable power source, wireless transceiver andanalog/digital multiplexers reside within both earpieces and provideevenly weighted distribution to wearer.

As described herein, the comparing means compares the sensory inputstimulus recorded by the one or more sensors with the database of one ormore user-specific sensory thresholds to obtain a sensory resolution fora user. The comparing means performs the aforementioned functions asfollows.

The user-specific thresholds can be obtained by having the user completea decision-tree styled survey (similar in scope to the survey describedin the Sustained Attention to Response Test (SART) protocol describedherein), and then a microprocessor measures the user-specific thresholdsagainst ecological and physiological/psychophysiological data streams todeliver appropriate intervention assistance. In an exemplary embodiment,the user-specific thresholds can dynamically change using a machinelearning capability.

An exemplary embodiment of how input stimulus is compared to stored datato generate user-specific interventions is illustrated via a blockdiagram in FIG. 16 , but this application is not limited thereto. In theexemplary embodiment illustrated in FIG. 16 , six components make up thewearable device's input section and include: an optical module 301; aninertial measurement unit (IMU) 304; an audio sensor 305; a galvanicmodule 306; a temperature sensor 309; and a biopotential analogue frontend (AFE) 310. In combination, these components deliver both ecological(environmental) and physiological/psychophysiological data to a sensorhub 311 (multiplexer), and the data is processed (typically throughwireless, bi-directional communication, though it can be directlyconnected) with the system's microprocessor (e.g., ARM Cortex) for rapidanalysis and comparison to existing thresholds, characteristics, anduser-preferences. The microprocessor 312 (e.g., ARM microprocessor) thendelivers the appropriate commands for interventional activities to beprocessed by those related system components as described herein. Thesix components are further described as follows.

The optical module 301 includes: (i) an inward facing pair of infraredsensors 302 that monitor pupillary response, portending to a user'sfocus and attentional lability; and (ii) a single outward facing sensorto determine ecological/environmental cues of a visual nature. A tunedoptical AFE 303 provides the appropriate pupillary data stream forprocessing and simultaneously provides an environmental data stream forimage recognition allowing the microprocessor 312 to determine visualenvironmental cues for which the user is responding. In both cases,image recognition (whether pupillary response, saccades, computerscreen, books, automobile roadways, office/academic surroundings, etc.)rely on a computer vision technique that allows the microprocessor 312to interpret and categorize what is seen in the visual data stream. Thistype of image classification (or labelling) is a core task andfoundational component in comparing real-time visual input to alibrary/catalogue of pre-labelled images that are interpreted and thenserve as the basis for an intervention, provided that the user'sthresholds are exceeded (or unsurpassed).

The IMU 304 measures and reports a body's specific force (in this case,the user's head/face). It also provides angular rate and orientationusing a combination of accelerometers, gyroscopes, and magnetometers todeliver a data stream relating to the user's head sway and attentionalfocus, when compared and contrasted to the optical AFE 303 and processedsimilarly against pre-labelled and classified data. In someimplementations, the IMU can include a 3-axis gyroscope/accelerometer.

Similar in scope to the optical module 301, the audio sensor(s) 305provide environmental data streams of a sonic nature which can becompared to known aural signatures that have been labelled and availablefor computer micro processing. The aural signatures that reachfrequency, amplitude, spatial, time-delay/phase and similaruser-selected thresholds could then be delivered for interventionalprocessing.

Both the galvanic module 306 and temperature sensor 309 providephysiological/psychophysiological and ambient/physiological data streamsthat measures the wearer's electrodermal activity (EDA), galvanic skinresponse (GSR), body and ambient temperature. These are utilized incombination with the biopotential AFE 310 resulting in real-time andcontinuous monitoring of the wearer's electrical skin properties, heartrate, respiratory rate, and blood pressure detection. Like the previoussensors, all are timestamped/synchronized for microprocessor processing,analysis, labelling/comparison and interventional activation.

The biopotential AFE 310 provides electrocardiogram (ECG) waveforms,heart rate and respiration, which in turn, feeds forward to themicroprocessor 312 to assist with a user'sphysiological/psychophysiological state, processing, and attentionalfocus/anxiety/fatigue intervention(s).

An additional block diagram providing additional microprocessor details(ARM processor) is illustrated in FIG. 17 .

A catalogue of user-specific cues and resolutions can be stored in adatabase in communication with the software stored in and executed fromthe wearable device and the control program/app, and available formachine learning purposes providing the application and hardware withever-increasing understanding of user environments and physiology cues,alerts, filters, and guidance. An artificial intelligence (AI) algorithmcontinuously processes user personalization, input cues and uniquelycrafted resolutions to further narrow and accurately predict and respondto physiological/psychophysiological input and responses. This machinelearning and AI algorithms increase user training and promote greaterautonomy, comfort, alertness, focus and mental health. The catalogue isavailable for user and professional analyses, data streams and progressreports are available for clinical study, medicalpractitioner/telemedicine, evaluation, and further review.

In some implementations, the wearable device preferences can be modifiedby the user to optimize device battery life. For example, the device canbe configured to operate in a power saving mode that conserves batterylife by making the sensor(s) less sensitive, limits power for less usedoperations, or otherwise operates in a manner to maximize battery life.Alternatively, the user can have the option of selecting an enhancedprocessing mode that emphasizes processing (e.g., makes the sensor(s)more sensitive) but uses more battery per unit of time.

In some implementations, the wearable device can be associated with anapplication that provides diagnostic data relating to the user, system,or for a caretaker/healthcare professional. For example, user diagnosticdata can include user preferences, user responsivity, and generatedissues and warnings. System diagnostic data can include environment anddevice responsivity, and issues and warnings. Caretaker/healthcareprofessional diagnostic data can include user efficacy performance(e.g., sonic, visual, or interoceptive), and any areas of concern suchas wearer guidance or device guidance.

As alluded to above, real-time filtering of audio signals can beimplemented in response to collecting sensory data from one or moresensors of the wearable device. As contrasted with adjusting time oroverall amplitude of the signal experienced by the listener, thisfiltering can take place in the frequency domain and affect at least acenter frequency (Hz), a cut or boost (dB), and/or a width (Q). Forexample, all low frequency hum associated with a real-time detection ofmachinery and/or light ballasts in an environment can be eliminatedand/or otherwise reduced, minimized and/or mitigated. This can beimplemented by adjusting, in real-time, the offending and nearbyfrequencies, either with a band-pass, or low cut, high pass filter, withspecific adjustments fine-tuned to the user's personalized profile. Insome implementations audio filtering can also apply to additionaldomains, including time, amplitude, and spatial positioning (e.g., tofilter distracting sounds that modulate from a given direction).

While some implementations have been primarily described in the contextof modifying and/or filtering distracting sounds (i.e., audiointerventions), the technology described herein can implement a similarset of interventions related to visual stimuli, either separately or incombination with other types of stimuli. For example, interventions suchas alerts, guidance, and/or combinations without filtering mediationscan be implemented. As further described below, visual interventions canbe based upon pupillary response, accelerometers, IMU, GSR detection,and/or video of the wearer's environment. In some implementations,identified visual interventions can work in concert with audiomodifications.

FIG. 23 depicts an example wearable device 500 that can be utilized toprovide visual interventions, in accordance with some implementations ofthe disclosure. As depicted, wearable device 500 can include the sensorsand/or transducers of wearable device 10. To support certain visualinterventions, wearable device 500 also includes a camera 550 anddisplay 551. As such, wearable device 500 is implemented as a wearableHMD. Although a glasses form factor is shown, the HMD can be implementedin a variety of other form factors such as, for example, a headset,goggles, a visor, combinations thereof and the like. Although depictedas a binocular HMD, in some implementations the wearable device can beimplemented as a monocular HMD.

Display 551 can be implemented as an optical see-through display such asa transparent LED and/or OLED screen that uses a waveguide to displayvirtual objects overlaid over the real-world environment. Alternatively,display 551 can be implemented as a video see-through displaysupplementing video of the user's real world environment with overlaidvirtual objects. For example, it can overlay virtual objects on videocaptured by camera 550 that is aligned with the field of view of theHMD.

The integrated camera 550 can capture video of the environment from thepoint of view of the wearer/user of wearable device 500. As such, asfurther discussed below, the live video/image feed of the camera can beused as one input to detect visual objects that the user is potentiallyvisually sensitive to, and trigger a visual intervention.

In some implementations, real-time overlay interventions can beimplemented whereby visual objects and/or optical interruptions aremuted, squelched, minimized, mitigated and/or otherwise removed from awearer's field of vision. In some implementations, the system'stransducer components (e.g., microphones and/or outward facing optics)can be used in concert with on-board biological sensors and/orprojection techniques that train/detect, analyze/match/predict, and/ormodify optical cues and/or visible items that correlate to a wearer'svisual sensitivity, attention, fatigue and/or anxiety thresholds. Insome implementations, disrupting visual, optical, and/or related scenerycan be filtered in real-time such that a wearer does not notice thatwhich is distracting.

In particular embodiments, one or two types of real-time opticalenhancement (REOPEN) algorithms can be implemented to detect, predict,and/or modify visual inputs that decrease distraction/mental healthissues and increase attention, calmness, and/or focus. The algorithmscan provide real-time (i) live-editing of visual scenes, imagery and/orobject and advanced notification for distracting optics that match auser's visual profile; and/or, (ii) live-modification of visualdistractions without advanced notification. Interventions that can bedelivered in real time using a REOPEN algorithm are illustrated by FIGS.24A-24C, further discussed below.

In one embodiment, a Realtime Optical Enhancement and Visual AprioriIntervention Algorithm (REOPEN-VAIL) can be implemented to train/detect,analyze/match/predict, and/or modify visual items that a user deemsdistracting (e.g., based upon a previously-described and/or createdpersonalized preferences profile) and compares these to prior and/orcurrent physiological/psychophysiological responses to the environment.Upon detecting a threshold crossing and/or match between interoceptivereactivity (egocentric) and visual cue (exocentric video) detection,REOPEN-VAIL can provide iterative analysis, training, enhancement,contextual modification, and/or advanced warning of optical distractionsprior to the wearer's ability to sense these visual and/or relatedphysiological/psychophysiological cues.

In one embodiment, a Visual A Posteriori Intervention Algorithm (VASILI)can be implemented to use multimodal learning methods to train/detect,analyze/match and/or modify visual items that previously a user deemsdistracting (e.g., based upon previously described or createdpreferences, prior and/or current physiological/psychophysiologicalresponses, etc.). In the case of VASILI, as contrasted with REOPEN-VAIL,the optics provide contextual modifications without advanced warning ofdistractibility in the form of interventions that are deliveredfollowing the system's identification of either ecological and/or thewearer's physiological/psychophysiological cue(s), in real-time afterthe user has been exposed to the visual distraction, and as part of aniterative process that can serve as a basis for future training,sensing, and/or apriori algorithms.

Various interventions can potentially be delivered in real time using aREOPEN algorithm. For example, as depicted by FIG. 24A, in response todetection of a certain visual object (e.g., a distracting visual objectand/or visual anomaly), haptic alerts, tone alerts, guidance alerts,combinations thereof and the like can be delivered to notify the wearer.The guidance alerts can provide user-selectable verbal instructions ofanticipated visual distraction and/or coaching to intervene withcontinued focus, calmness, and/or attention. The aforementioned guidancealerts can be implemented as visual and/or text based guidance that isviewable to the wearer, via a displayed (e.g., using display 551) userselectable system visible to one and/or both eyes and/or sightlines tointervene with continued focus, calmness, attention, combinationsthereof and the like. (e.g., FIG. 24B).

In some implementations, visual distractions (e.g., certain objects,faces, etc.) can be rendered such that they appear as opaque and/orobscure. This blurring effect can blur the identified, distracting,and/or otherwise offending image as a user-selectable and/or predefinedintervention affecting what the wearer sees. (e.g., FIG. 24B).

In some implementations, a visual scene can be rendered with a modifiedbackground, eliminating the visual distraction, and/or identified image.The system can interpolate nearby images to the distracting objectand/or replicate the background by overlaying a “stitched” series ofimages that naturally conceal and/or suppress the sensory effects of theoffending optics, all of which are user-selectable. (e.g., FIG. 24A).

In some implementations, a predetermined, user-selectable emoticonand/or place-holder image can be rendered that camouflages thedistracting optic and/or visual disruption. (e.g., FIG. 24B).

In some implementations, a visual distraction can be rendered with amodified color palette and/or related pigmentation can be modified touser-preference to reduce the effects of distraction, sensitivity,focus, anxiety, and/or fatigue, and/or combinations thereof and the like(e.g., FIG. 24C).

In some implementations, a visual distraction can be rendered withedited brightness and/or sharpening of images that are user-selectableas either muted and/or modified visuals (e.g., FIG. 24C).

In some implementations, a visual distraction can be rendered with anedited size such that images are augmented and/or modified such theybecome more prominent, larger, and/or highly visible (e.g., FIG. 24C).

The principles of the present invention includes certain exemplaryfeatures and embodiments, and effects thereof, will now be described byreference to the following non-limiting examples.

FIG. 25 depicts one particular example of a workflow that uses a REOPENalgorithm to provide interventions, in real-time, in a scenario wherethere is a singular distracting visual source (e.g., birds flying acrossthe sky and causing the individual to become unfocused from work). Inthis example, the algorithm is implemented using a convolutional neuralnetwork (CNN). As depicted, distracting stimuli can be visualized by theindividual wearing a wearable device (e.g., wearable device 500) thatcaptures cues and then processes and trains on that data (e.g.,environmental and/or psychophysiology). Convolution layers of the CNNare formed and/or iteratively examined, for example, visual data thattriggers the distraction—or physiology such as pupillary movement,including edges, shapes, and/or directional movement. Connected layersdigitize the prior convolutional layers. This can be repeated formultimodal data types such that when layers correlate to pre-defined“eyes on target”, a learned state of focused activity can be recorded.Conversely, when pupillary movement is uncoordinated with a targetand/or activity, a separate learned event can be memorialized and/ortagged as unfocused activity, resulting in delivery of a digitalmediation until a “focused” condition is observed.

For example, in the case of a REOPEN-VAIL algorithm, an aprioriintervention could be one that has already been trained on the flowdepicted in FIG. 25 , and then sensed by the system pursuant to asimilar external cue and/or an early reflection of pupillary unfocusedprediction. This could generate an alert prior to the actual long-termindividual state in an attempt to mediate prior to distractibility. Inthe event of a failure in the REOPEN-VAIL (e.g., the individualcontinues to remain out of focus for a period of time, e.g., greaterthan about 5 seconds or otherwise dictated by the personalizedparadigm), the posteriori flow could repeat, this time offeringmediations consisting of alerts, guidance, and potentially filtering tomute, eliminate, mitigate, minimize and/or otherwise modify theoffending distraction.

Participant Public Information (PPI) Study

A Participant Public Information (PPI) study was conducted to identifydependent/thematic variables and dependent/demographic factors relatedto the utilization of the wearable device. The PPI participants includedverbally able, autistic and neurotypical adolescents and adults aged15-84. All participants had intelligence in the normal or above averagerange and the majority were living independent lives, i.e., studyparticipants did not fall into the general learning disabilities range.Participants provided health/medical conditions and disabilityinformation relevant to their opinions about distractibility, focus andanxiety at both school and work. Before the study, participants providedinformed consent along with a verifiable ASC diagnosis, whereapplicable. All participants were invited to take part in a Focus Group,User Survey Group or both.

The Focus Group included 15 participants, ages 17-43, and participantsstudied distractibility and attentional focus. The main task of theFocus Group was to comment on sensory issues and provide input into thedesign of a user survey to ensure relevance to autism and adherence toan autism-friendly format.

The User Survey Group included 187 participants, ages 18-49, andprovided first-person perspectives on distractibility and focus whilegathering views and opinions of which aspects of technologicalaid/support would be most welcomed and have the biggest impact onsensory, attentional, and quality of life issues.

Embedded within the PPI study was a Lived Experience Attention AnxietySensory Survey (LEA²Se) developed for participant expression and used asa preparatory point to discuss focus, distractibility, anxiety, sensoryand attentional difficulties, and needs. The LEA²Se participants wereencouraged to specify interests, attitudes, and opinions about receivingtechnology supports. This study was dispensed online.

Pre-Trial Battery Examination (PTBE)

196 autistic participants were recruited via opportunity sampling. Afterexclusion, 188 participants were left (109 males, 79 females), of which12.2% were 18-20 years old, 21.2% were 21-29 years, 60.8% were 30-39years and 5.8% were 40-49 years old. For the purpose of the PTBE,variables that tap into different aspects of an autistic individual'sexperience were designed. After identifying the variables of interest,questions which addressed these variables were allocated a numericalvariable. Some questions were not allocated to any variable, andtherefore were not analyzed in this study. Table 1 shows the resultantvariables, the number of questions that fell under each variable, and anexample of the type of questions used to investigate that variable. Eachparticipant received a score for each of these variables, which wascalculated by averaging their responses to the questions that fell underthat variable. The variables are mutually exclusive (i.e., no questionwas included in the computation of more than one variable). To assessvalidity of the variables, inter-item correlations for each variablewere investigated, all of which had a Cronbach's alpha greater than0.80, which demonstrates high internal validity.

TABLE 1 No. of Cronbach’s Variable Items Alpha Sample QuestionSensitivity Impact (SI) 9 .871 “I have considered abandoning orinterrupting my job/employment or academic studies because ofsensitivity to my environment” Anxiety Proneness (AP) 25 .947 “Certainsounds, sights or stimuli make me feel nervous, anxious or on edge”Distractibility Quotient (DQ) 9 .839 “I often begin new tasks and leavethem uncompleted” Technology Tolerance (TT) 11 .885 “I think I wouldenjoy owning a wearable device if it helped reduce anxiety, lessendistraction or increase focus at work, school, seminars, meeting orother locations” Visual Difficulty Quotient (VDQ) 4 .919 “I havedifficulty in bright colourful or dimly lit rooms” Sound DifficultyQuotient (SDQ) 6 .821 “I find sounds that startle me or that areunexpected as . . . ” (distracting-not distracting) PhysiologicalDifficulty 3 .925 “My sensitivity sometimes causes my heart rate toQuotient (PDQ) speed up or slowdown”

Various pilot outcomes using benign data are depicted in FIGS. 2 and 3 .These graphics report sensitivity across three modalities (visual, auraland anxiety) along with wearable interest among ASC participants forvisually distracting stimuli. Kruskal-Wallis H testing on the studypopulation indicated:

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, visualdifficulties and physiological/psychophysiological difficulties, but nostatistically significant difference in sound difficulties, based onage;

a statistically significant difference in sensitivity impact and sounddifficulties, but no statistically significant difference in anxietyproneness, distractibility quotient, technology tolerance, visualdifficulties, physiological/psychophysiological difficulties, based ongender;

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, sounddifficulties, visual difficulties and physiological/psychophysiologicaldifficulties, based on education level; and

a statistically significant difference in sensitivity impact, anxietyproneness, distractibility quotient, technology tolerance, sounddifficulties, visual difficulties, but no statistically significantdifference in physiological/psychophysiological difficulties, based ondifferent employment levels.

Sustained Attention to Response Test (SART)

A SART study was conducted subsequent to the PPI study and PTBE. Thestudy included online testing designed to test sensory issues affectingparticipants diagnosed or identifying with ASC. Specifically, this studyexamined a subset of components within a wearable prototype to answertwo questions: (i) is it possible to classify and predict autisticreactivity/responsiveness to auditory (ecological) disturbances andphysiological/psychophysiological distractors when autistic individualsare assisted through alerts, filters and guidance; and (ii) can theexploration of Multimodal Learning Analytics (MMLA) combined withsupervised artificial intelligence/machine learning contribute towardunderstanding autism's heterogeneity with high accuracy therebyincreasing attentional focus whilst decreasing distractibility andanxiety?

This study is grounded in Attention Schema, Zone of ProximalDevelopment, Multimodal Discourse Analysis and Multimodal LearningAnalytics theories, and makes use of both evaluator-participatory anduser-participatory methodologies including iterative development andevaluation, early-user integration, phenomena of interest and persistentcollaboration methodologies.

In an exemplary study protocol, baseline testing and related scores arederived both procedurally on pre- and post-subtests to create putative,cognitive conflicts during subtests that may result in a hypothesizedand measurable uptick in both distractibility and anxiety.Simultaneously, this upsurge will likely pool with diminished focus andconical attentional performance. Finally, and during the lattersubtests, a “confederate” (human wizard) will present a collection ofhand-crafted alerts, filters and guidance. These will emulate theoperation of the wearable intervention by offsetting andcounterbalancing distracting aural stimuli. To reduce fatigue effect,these interventions will either exist in counterbalanced, randomized andpossibly multiple sessions. Alternatively, a combination of alerts,filters and guidance will be provisioned to lessen overall length of theexperiments, as shown in Table 2 below and illustrated in FIG. 4 .

The delivery of isolated and permutated support may produce broadmeasures of test responses. Mixed method (qualitative and quantitativeevaluations) combined with participants' overt behaviors obtainedthrough audio and video recordings may provision coding and analysiswith sample accuracy synchronization to the systems software. Dependingupon sample size and time constraints, this design considers post-hocvideo analyses (e.g., participant walk-through) in either a structuredor liberal form. These examinations may help facilitate recall,precision and provide further understanding of anxiety and otherepisodic testing moments.

TABLE 2 Test Description Baseline SART Standard sustained attention testwithout sonic disturbances. Subtests including interventions Subtest I.:Standard sustained attention test (SART) with sonic disturbance. SubtestII.: SART with combined filters and sonic disturbances. Subtest III.:SART with combined alerts and sonic disturbances. Subtest IV.: SART withcombined guidance and sonic disturbances. Subtest V.: SART with combinedfilters, alerts, guidance and sonic disturbances. Follow-on baselineSART Standard sustained attention test without sonic disturbances.

This study tests a sub-system mock-up using multimodal, artificialintelligence-driven (MM/AI) sensors designed to provide personalizedalerts, filters, and guidance to help lessen distractibility and anxietywhilst increasing focus and attention by enhancing cognitive loadrelated to unexpected ecological and physiological/psychophysiologicalstimuli. The study uses a series of online experiments in which thewearable's operation is simulated by a confederate, human operator. Thisstudy proposes within-subjects, two-condition SART employing multimodalsensors during which a user's performance is measured (Robertson, I. H.,Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). ‘Oops!’:Performance correlates of everyday attentional failures in traumaticbrain injured and normal subjects. Neuropsychologia, 35(6), 747-758).Importantly, tasks were performed, and data was collected, with andwithout the effects of distracting sonic stimuli (the singular modality)accompanied by various combinations of advanced alerts, audio filteringand return-to-task guidance. This phase of the study included forty (40)participants, including 19 autistic participants and 21 non-autisticparticipants.

The classic SART paradigm, which is regarded as an exemplar of both highreliability and validity, requires participants to withhold pressing acomputer key during the on-screen appearance of a target image. Thisstudy modifies SART by flipping the keystroke sequence; that is, ratherthan holding a key “down” throughout the majority of the test, theassigned key was depressed only when a target appeared. This providedgreater reliability and reproducibility when testing at distance andonline (Anwyl-Irvine, A. L., Massonié J., Flitton, A., Kirkham, N. Z.,Evershed, J. K. (2019). Gorilla in our midst: an online behaviouralexperiment builder. Behavior Research Methods). Performance on the SARTcorrelates significantly with performance on tests of sustainedattention. Research indicates that SART does not, however, correlatewell to other types of attentional measures, “supporting the view that[SART] is indeed a measure of sustained attention” (Robertson et al.,1997, 747, 756). This study employs SART specifically because studieslike Robertson's corroborate that this methodology is fundamentallyimpervious to effects of age, estimated intelligence scoring or otherintellectual measures.

Additionally, and within this study, SART tasks are performed, and datais collected, with and without the effects of distracting sonic stimuli.This modality serves as both the singular and irrelevant foil, whenaccompanied by various subtest combinations of advanced alerts, audiofiltering and return-to-task guidance models. These combinations serveas the intervention(s). The study subtests exploit visual search oftargets against competing and irrelevant foils (e.g., alpha-numeric).Supplementing these textual targets with additional contestingmodalities (e.g., sonic foils and interventions) makes this SART studynovel compared to previously-conducted studies. SART requiresparticipants to “actively inhibit competing distractors and selectiveactivation of the target representation. Memory factors are minimal inthese tasks, as the targets are simple and are prominently displayed tosubjects in the course of testing” (Robertson, I. H., Ward, T.,Ridgeway, V., & Nimmo-Smith, I. (1996). The structure of normal humanattention: The Test of Everyday Attention. Journal of the InternationalNeuropsychological Society, 2(6), 525, 526). Though reaction time andother temporal measures are considered in developing participant scores,this study rules out the possibility that subtests only measure samplingspeed of processing as qualitative mental health measures are alsointegrated.

The first PPI study and PTBE facilitated a deeper understanding of thelived experiences of autistic individuals' and their focus,distractibility and anxiety concerns with a particular focus onlater-life, educational and workplace experiences. The PPI study andPTBE also provided information regarding a potential decrease in bothanxiety and sensitivity as autistic people age, and that these trendsdiffer within specific modalities. Stability is achieved across variousages for a sonic variable but varies for both visual andphysiological/psychophysiological variables. Further, anxiety andsensitivity may not relate across gender. And while there are downwardaging trends in both technology tolerance and distractibility, there isvariation in ages 30-39 perhaps due to the massive size of thisparticular sample.

The study design is rooted in a SART/WoZ design and includes onlineexperiments whereby system operations were simulated by a human operatorarmed with prior, hand-crafted interventions and scripts that supportparticipants' testing (Bernsen, N. O., Dybkjær, H., & Dybkjær, L.(1994). Wizard of Oz prototyping: How and when. Proc. CCI Working PapersCognit. Sci./HCl, Roskilde, Denmark). The Wizard of Oz (WoZ) studydesign provides economical and rapid implementation and evaluation, andhas gained academic acceptance and popularity for decades. (Bernsen etal., 1994); (Robertson et al., 1997); (Fiedler, A., Gabsdil, M., &Horacek, H. (2004, August). A tool for supporting progressive refinementof wizard-of-oz experiments in natural language. In Internationalconference on intelligent tutoring systems (pp. 325-335)); (Maulsby, D.,Greenberg, S., & Mander, R. (1993, May). Prototyping an intelligentagent through Wizard of Oz. In Proceedings of the INTERACT'93 and CHI'93conference on Human factors in computing systems (pp. 277-284)). Thesesupports may lessen distractibility/anxiety whilst increasing attentionby enhancing cognitive load related to unexpected stimuli. WoZ proposesa within-subjects, two-condition SART employing multimodal sensorsduring which a user's errors of commission, errors of omission, reactiontime, state-anxiety, and fatigue levels are computed. (Burchi, E., &Hollander, E. (2019). Anxiety in Autism Spectrum Disorder); (Ruttenberg,D. (2020). The SensorAble Project: A multi-sensory, assistive technologythat filters distractions and increases focus for individuals diagnosedwith Autism Spectrum Condition. MPhil/PhD Upgrade Report. UniversityCollege London).

Memory factors are minimized in SART testing, as visual tasks are modestand tried out by participants prior to testing. Though intervallicmeasures are included in scoring participant performance, there areother critical metrics resulting from both qualitative and quantitativescoring. As mentioned in Robertson (1996), these do not create cognitiveburdens of similar dynamics and characteristics; therefore, they do notconstitute a myopic or simplified sampling speed of processing measure.Further, this study separates visual sustained tasks from auditorydistractions, which in turn avoids cross-modality and interferenceconcerns.

The study utilized t-test/correlation point biserial models (Faul, F.,Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexiblestatistical power analysis program for the social, behavioral, andbiomedical sciences. Behavior Research Methods, 39, 175-191).Computations were based upon a given a, power and effect size.Generally, T-tests are calculations that inform the significance of thedifferences between groups. In this case, it answers whether or not thedata between autistic and non-autistic scores (measured in means) couldhave happened by chance. Alpha (a) is a threshold value used to judgewhether a test statistic is statistically significant, and was selectedby the inventor (typically 0.05). A statistically significant testresult (p=probability and p≤0.05) indicates that the test hypothesis isfalse or should be rejected, and a p-value greater than 0.05 means thatno effect was observed. The statistical power of a significance test(t-test) depends on: (i) the sample size (N), such that when Nincreases, the power increases; (ii) the significance level (a), suchthat when a increases, the power increases; and (iii) the effect size,such that when the effect size increases, the power increases.

Half the sample included neurotypical participants and half identifiedas or possessed an ASC diagnoses. All participants utilized pre/post WoZmanipulations. Baseline testing and related scores were derived bothprocedurally on pre- and post-subtests. Putative, cognitive conflictsduring subtests that may result in a hypothesized and measurable uptickin both distractibility and anxiety were created. Simultaneously, thisupsurge likely pooled with diminished focus and conical attentionalperformance.

Finally, and during the latter subtests, a “confederate” (human wizard)presented a collection of hand-crafted alerts, filters and guidance.These emulated the operation of the wearable intervention by offsettingand counterbalancing distracting aural stimuli. To reduce fatigueeffect, these interventions existed in counterbalanced, randomized andmultiple sessions. Alternatively, a combination of alerts, filters andguidance were provisioned to lessen overall length of the experiments,as illustrated in FIG. 3 .

The delivery of isolated and permutated support produced broad measuresof test responses. Mixed method (qualitative and quantitativeevaluations) combined with participants' overt behaviors obtainedthrough audio and video recordings provisioned coding and analysis withsample accuracy synchronization to the systems software. Depending uponsample size and time constraints, this design considers post-hoc videoanalyses (e.g., participant walk-through) in either a structured orliberal form. These examinations may help facilitate recall, precisionand provide further understanding of anxiety and other episodic testingmoments.

Reliability was tested by administering the procedure to a sub-group ofautistic and non-autistic subjects on one occasion over a period of 7separate trials. The I. H. Robertson protocol was used owing to itsheritage and wide acceptance in the scientific community (Robertson etal., 1997).

In the SART procedure, 100 single letters (e.g., A through Z) werepresented visually for up to a 5-minute period. Each letter waspresented for 250-msec, followed by a 900-msec mask. Subjects respondedwith a key press for each letter, except 10 occasions when the letter“X” appeared, where they had to withhold (inhibit) a response. Subjectsused their preferred hand. The target letter was distributed throughoutthe 100 trials in a non-fixed, randomized fashion. The period betweensuccessive letter onset was 1150-msec. Subjects were asked to giveimportance to accuracy first followed by speed in doing the task.

The letters were equally presented in identical fonts (Arial) and size(36 point) corresponding to a height of 12.700008 mm. The mask followingeach digit consisted of a white square with no border or fill coloring.The total area of the mask was dependent upon the user's screen size(e.g., the entire screen would be considered the maskable area). By wayof comparison, a 10-inch diagonal screen would produce a 25 cm diagonalmask for a laptop and a 40 cm diagonal mask for a tablet. Similarly, a15-inch diagonal and 20-inch diagonal screen would produce a 38.10 cmmask and 50.80 cm mask for both laptop and tablet, respectively.

Each session was preceded by a practice period consisting of 15presentations of letters, two of which were targets. Further, aself-assessed state-trait anxiety and state-trait fatigue inventory(STAFI) was conducted prior to and following each of the seven SART/WoZtrials (Spielberger, C. D. (1972). Conceptual and methodological issuesin research on anxiety. Anxiety: Current Trends in Theory and Researchon Anxiety). The fatigue portion in the more commonly used anxiety onlyinventory was combined to measure trait and state anxiety and fatigue.STAFI have been historically used in clinical settings to diagnoseanxiety and to distinguish it from depressive syndromes. The STAFI isappropriate for those who have at least a sixth grade reading level(American Psychological Association. (2011). The State-Trait AnxietyInventory (STAT). American Psychological Association).

Participants selected from five state anxiety items includingillustrations and text that depicted how they were feeling at the momentof query, including: “1—Extremely anxious”, “2—Slightly anxious”,“3—Neither anxious nor calm”, “4—Slightly calm” or “5—Extremely calm”;“I am worried”; “I feel calm”; I feel secure.” Lower scores indicatedgreater anxiety.

Similarly, five fatigue items included illustrations and text thatdepicted feelings at the moment of query, including: “1—Extremelytired”, “2—Slightly tired”, “3—Neither awake nor tired”, “4—Slightlyawake”, or “5—Extremely awake”. Lower scores indicate greater fatigue.

Performance on the SART clearly requires the ability to inhibit orwithhold a response. This is made more difficult when distractors areintroduced into the testing paradigm. Specifically, hand-crafted sonicsof varying amplitude, frequency, time/length, distortion, localization,and phase were introduced to mimic those sounds that might occur inoffice, workplace, education, and scholastic settings.

A total of twenty-eight (28) sound sources were played over a durationof five-minutes and included office industrial, fire alarms, telephoneringing, busy signals and dial tones, classroom lectures, photocopierand telefacsimile operations, footsteps, sneezes, coughs, pencilscribbling, and the like.

Prior to testing, this study accomplished similar testing throughpre-programmed sensing and related interventions. The scripting of sonicstimuli, along with fabricated participant alerts, filters and guidancewere operationalized to give the sensation and response of customizedinterventional support. These smart-system components were pre-defined,and the sensor cause and effect become evaluative to stabilize systemoperation, encourage autonomous testing and synchronized data recording.As in Forbes-Riley, K. and Litman, D. 2011, Designing and evaluating awizarded uncertainty-adaptive spoken dialogue tutoring system, ComputerSpeech & Language 25, 105-126, this study leverages WoZ in place ofmultiple system components; the combination of which presents a fullyintelligent and integrated system. The human wizard is predominantly aconductor/evaluator whose functions and monitoring of programmaticmaterials are unidentified to the participant. Users make selectionsthrough a “dumb” control panel, provisioning their customized alerts,filters and guidance. Importantly, the mechanism advances autonomy byproviding specific functionalities for participant evaluation, whilstostensibly eliminating evaluator influence. In selecting thesecomponents, the following questions are reviewed: What requirementsshould the evaluator meet before conducting a study? How does theevaluator follow the plan, and what measurements will reflect test andsub-test flow? How should control panel component be designed, and howwould this affect its operation? How does the evaluator's personalbehavior affect system operation?

All studies were administered and hosted in the Gorilla IntegratedDevelopment Environment (IDE) and available through most common webbrowsers and appliances (Anwyl-Irving et al., 2019). All audio, videoand related ecological/interoceptive data were presented and collectedin real time via the IDE and evaluator.

Study Variables:

The overarching study was divided into four components including: (i)the PPI study and PTBE described earlier; (ii) the evaluator (includingtasks, self-reports and controls); (iii) the system prototype (anon-wearable sub-system); and (iv) the participants (who were recorded).Study variables are listed in Table 3A, and illustrated in FIG. 18 :

TABLE 3A Variable Type Filter Independent Alert Guidance SystemParticipant Evaluator Interventional combinations: assistance DependentImprovement: focus Improvement: attention Improvement: technologytolerance Reduction: anxiety Reduction: distractibility Reduction:discomfort

SART/Wizard of Oz Protocol Design:

FIG. 6 is a flowchart illustrating the SART/WoZ Protocol used in thisstudy, and includes four higher-order classes that include study aims,variables, assessments, and outcome measures. Study questions,independent and dependent variable, potential assessments/activities andexpected results are also depicted. Based upon this SART/WoZ Protocoldesign, the corresponding class descriptions are listed in Table 3B:

TABLE 3B Class Descriptors Aims How effective are alerts, filters &guidance in improving intention, reducing both distraction and anxietyin ASC individuals? How tolerable is an Ai/MMLA wearable (even as a WOz)in mitigating sensory issues? Can a single modality system be replicatedsuccessfully across multimodalities? What variables influence each typeof intervention? Variables See Table 3A above Assessments SustainedAttention to Response Task (SART) at baseline no distraction orintervention followed by state anxiety Likert assessment. SustainedAttention to Response Task (SART) at with audio foils and-either: (i)varying interventions followed by state anxiety Likert assessment -or-(ii) combined interventions followed by state anxiety Likert assessment.Sustained Attention to Response Task (SART) return to baseline followedby state anxiety Likert assessment. Outcome Response time measuresAverage test time Percentage correct responses Anxiety/mental healthquotient

Testing Procedures:

Each participant took part in a single experimental session after firstcompleting consent and demographic forms. The session commenced with ashort (1-2 minute) tutorial to ensure that the participant wascomfortable with the proper operation of the testing software, and tointroduce the participant to the importance of staying within range ofthe web camera and pointing devices for proper monitoring of theenvironment and their physiology. After the tutorial, participants wereadvised that the evaluator was available throughout the session to helpmonitor the system and to answer any questions between tests.Participants were not advised of the evaluator's contribution to thetesting (WoZ), that any alerting, filtering or guidance programming waspre-defined prior to the experiment, or that their control of the systempreferences was of a placebo nature.

The WoZ testing (from baseline through multiple interventions and then areturn to baseline) included three phases. Phase I commenced withBaseline I cognitive testing; that is, there were neither distractingcues nor interventions. Phase II introduced accompanying filters, alertsand guidance applied in concert with randomized sonic distractions andtesting. Phase III reintroduced a return to baseline to ensure thatparticipants' recovery and responses were not memorized and thatrandomization effects were properly sustained.

Alerts, Filters and Guidance Structure:

The alerts and guidance of this study protocol utilizes Amazon Polly™, aneural text-to-speech (TTS) cloud service designed to increaseengagement and accessibility across multiple platforms (Neels, B.(2008). Polly. Retrieved Dec. 17, 2020, fromhttps://aws.amazon.com/polly/). Polly's outputs, as listed in Table 4,are cached within the testing system and portends personification of asafe, uncontroversial, newscaster speaking in a style that is tailoredto specific use cases.

TABLE 4 Stimulus Event/Cue Amazon Polly ™ Script Alert: distractinginteroceptive Hi. I sensed a physiological event that I wanted to alertyou to. Alert: distracting noise Hi. I've sensed a noise that maydistract you, and I wanted to alert you in advance. Alert: distractingvisual Hi. I've sensed a visual event that may distract you, and Iwanted to alert you in advance. Filter: distracting noise I am filteringthe noise to help you re-focus. Guidance: encouragement That's it. I amsensing that you're doing quite well at the moment and that you'refeeling more in control, relaxed and ready to resume your task.Guidance: encouragement 2 Good job. Guidance: encouragement 3 Well done.Guidance: encouragement 4 Congratulations. Keep up the great effort.Guidance: encouragement 5 I am proud of you. Guidance: filteringreminder By filtering noise, reminding you to take a deep breath andrelax your body, you can more easily return to your current task.Guidance: general re-focus Hi. I wanted to provide you with somefriendly guidance to help you re-focus now. Guidance: general relaxationI want to suggest you take a deep breath and relax your body position tohelp you re-focus. Guidance: motivational reminder If you're feelingtired or not motivated to focus on your work, perhaps a few deepbreaths, combined with a quick stretch or standing up might be useful.Guidance: re-focus reminder I am providing this reminder to help youre-focus. Guidance: self-error Oops, I made a mistake. Sorry . . . I'mstill learning what you might find distracting. The more I work for you,the more accurate I'll become. Thanks for understanding.

A single modality of varying sonic distractions was scheduled fortesting during this study. While both sonic and visual cues can easilybe programmed, for fidelity and deeper understanding, the experimentswere conducted with audio cues only. The stimuli events and cues arelisted in Table 5, along with their accompanying filter name anddescription. The success and efficacy of a prototype wearable device,according to an exemplary embodiment, can be assessed on the basis ofparticipant data collected (both quantitative and qualitative) duringtesting administration of these stimulus event and the participant'sperformance.

TABLE 5 Stimulus Event /Cue Filter type Description Sonic: Spatialambiguity Sonic imager Psycho-acoustic spatial imaging adjustment toenhance, alter or eliminate stereo separation. Sonic: Amplitudedistortion Linear multiphase Adjusts adaptive thresholds, makeup Sonic:Amplitude over-modulation compressor gain, and finite response filtersacross Sonic: Amplitude features five user-definable under-modulationbands with linear phase crossovers for phase distortion-free, multibandcompression. Sonic: frequency Linear phase Up to five bands of bandanomaly (low) equalizer low band and Sonic: frequency broadbandfrequency band anomaly (low- reduction with Sonic: frequency nine phasetypes band anomaly (hi- Sonic: frequency band anomaly (hi) Sonic: timeanomaly C1 Expansion, gaining, (RT60 <50 Compressor and equalizationSonic: time anomaly (delay <30 sidechaining to eliminate sonic tailSonic: time anomaly (delay >30 through split-band dynamics, look Sonic:time anomaly (delay ahead transient processing >50-100 milliseconds) andphase correction. Sonic: phase distortion 1 < x < 30 In phase alignerReal time, dual waveform processing milliseconds for alignment,sidechain to external file, delay control to time compensation, phaseshift curve adjustments and correlation recovery.

Protocol Testing Measures:

Participants were instructed to remain in close proximity to theircomputer's web camera and in direct contact with at least one of theirpointing devices (e.g., mouse, trackpad, keyboard) at all times duringthe experiment. Participants were also informed that: measures ofengagement, focus, comfort, productivity, and autonomy would be tested;environmental and physiological/psychophysiological monitoring (e.g.,ecology and interoceptive) would occur during testing; and participanthead sway, pupillary responsivity, GSR, environmental sound and visionwould be collected.

Interventions:

As the study proceeded, participants received combinations of support byway of alerts prior to distraction and/or filtered audio cues (e.g.,distractions that are muted, spatially centered, etc.). Optionally,participants also received post-stimuli guidance to help them return totasks/activities/tests.

Data Collection Method:

This study utilized three data capturing methods—direct computerinput/scoring, video analysis and self-reporting. The first isintegrated in the Gorilla application, the second aims to record andmake possible observations of subjects' system interactions, and thethird may reflect the participant's and evaluator's operationexperiences (Goldman, N., Lin, I.-F., Weinstein, M. and Lin, Y.-H. 2003.Evaluating the quality of self-reports of hyperthension and diabetes.Journal of Clinical Epidemiology 56, 148-154).

Participants:

The PPI study of verbally abled, autistic (ASC) participants consentedto: (i) focus groups exploring distractibility/attention; and (ii) aLived Experience Attention Anxiety Sensory Survey (LEA²Se) indicatingfirst-person perspectives on sensory, attention and mental healthmeasures. LEA²Se was developed, customized and further modified byfifteen (15) participants who gave autistic voice related to sensory,attentional, and anxiety questions and issues. LEA²Se was then utilizedas the basis for the WoZ Proof-of-Concept/Trial (POC/T, N=5, 2=ASC,3=NT, 4/1=F/M) and final trials/experiments. The POC/T confirmedadequate systems operation, and translation from user interfaces to datacollection devices and downstream to analysis applications.

Following the POC/T implementation and prior to the SART/WoZ trials, thePTBE was administered (N=131; 71=ASC, 60=NT; 59=M, 72=F). Eachparticipant was given four discrete tests including the matrix reasoningitem bank (MaRs-IB): a novel, open-access abstract reasoning items foradolescents and adults; the Autism-Spectrum Quotient (AQ): a 50-itemself-report questionnaire for measuring the degree to which an adultwith normal intelligence has the traits associated with the autisticspectrum; and the Adult ADHD Self-Report Scale (ASRS A and ASRS B)Symptom Checklist: a self-reported questionnaire used to assist in thediagnosis of adult Attention Deficit Hyperactivity Disorder (ADHD) andspecifically daily issues relating to cognitive, academic, occupational,social and economic situations.

Based on the PTBE results, a well-matched cohort of SART/WoZparticipants were selected for demographics and test battery results.This yielded a nearly 50/50 balance in neurodifferences betweenexperiment and control groups (N=40; 19=ASC/21=NT;15=M/24=F/1=non-Binary) so that seven randomized, control trials ofpre/post sensory manipulation could take place.

Data analysis examined the use of variables derived from the PPI studyand PTBE described earlier to understand the lived experience ofautistic individuals relating to distractibility, attention, andanxiety. These variables and supporting data were used to predict howparticipants of differing ages and gender might perform on tasksaccompanied by distracting visual, audio, andphysiological/psychophysiological cues. These variables include:Sensitivity Impact; Anxiety Proneness; Distractibility Quotient; VisualDifficulty Quotient; Sound Difficulty Quotient;physiological/psychophysiological (Interoceptive) Difficulty Quotient;and Correlation. Of these, 6 variables, 3 of which are contextuallyrelated to different modalities, were tested in this study. Thedescriptive statistics and correlations for these variables are listedin Table 6:

TABLE 6 Variable Median IQR AP DQ SDQ VDQ PDQ Sensitivity Impact (SI)2.50 1.13 .872 .713 −.050 −.750 −.622 Anxiety Proneness (AP) 2.44 0.74.748 −0.86 −.753 −.619 Distractibility Quotient 2.56 1.33 −.241 −.822−.786 Visual Difficulty Quotient 5.50 3.50  .255  .217 Sound DifficultyQuotient 4.50 2.00  .866 Physiological Difficulty 5.67 5.00

Stepwise Regression:

Dummy variable were created for both age and gender (i.e., the onlydemographic factors that were not correlated), and were combined withsensitivity, anxiety and distractibility variables (SI, AP and DQ)embedded within a stepwise regression analysis to predict scores insound, visual and physiological/psychophysiological/interoceptivemodalities. The model(s) with the highest R2/significance are reportedin Table 7:

TABLE 7 Sound  Predictors: Distractibility Quotient, Gender, SensitivityImpact  Model Significance: F(3, 185) = 12.98, p < .001    DQ: t = −2.82p < .001 β = −.476    Gender: t = 3.94 p < .001 β = .272    SI: t = 2.32p < .021 β = .233  R = .419  R² = 17.5% Visual  Predictors:Distractibility Quotient, Sensitivity Impact  Model Significance: F(2,185) = 241.46, p < .001    DQ: t = −9.40 p < .001 β = −.528    SI: t =−6.89 p < .001 β = −.387  R = .85  R² = 72.4% Physiological  Predictors:Distractibility Quotient  Model Significance: F(1, 185) = 329.43, p <.001    DQ: t = −18.15 p < .001 β = −.800  R = .80  R² = 64%

Standard Regression

One categorical variable regressed (i.e., either age or gender,depending on which was significant in the previously conducted ANOVAs)with a continuous variable (either Sensitivity Impact [SI],Distractibility Quotient [DQ] or Anxiety Proneness [AP], depending onwhich had the highest correlation) onto the three modalities (e.g.,sound, visual and physiological/psychophysiological), all of which serveas dependent variables in this study. Standard regression values areshown in Table 8:

TABLE 8 Sound  More correlated to DQ than SI  Gender + DQ = R² of 15.1% Age is not correlated (ANOVA wasn't significant)  Anxiety proneness hashigher correlation, but not significant with gender Visual  Age + SI +DQ = R² of 73.5% (but DQ and SI are correlated)  Age + SI = R² of 64.1% Gender not significant in ANOVA Physiological  Age + DQ − R² = 65.7% Gender not significant

PTBE Results/Data Analysis:

For the initial run of SART/WoZ participants (N=37, mean age 25.70,S.D.=7.442), their mean PTBE scores ranked as follows: MaRs-IB=62.20%and 18.64; AQ=24.51 and 12.66; ASRS-1=3.19 and 1.66; and ASRS-2=5.51 and3.30). Independent samples tests for all PTBE results yielded MaRs-IB of(F=0.166, t=−0.295, df=35 and Sig. 2-tailed=0.769); AQ of (F=0.046,t=4.494, df=35 and Sig. 2-tailed=0.000), ASRS-1 of (F=0.281, t=2.757,df=35 and Sig. 2-tailed=0.009); and ASRS-2 of (F=0.596, t=2.749, df=35and Sig. 2-tailed=0.009). Demographic independent sample tests wereinsignificant across age, gender, handedness, education, employment,income, status, children, home, and location.

For PTBE participants (N=131; autistic=71, non-autistic=60, and thosewho were tapped for the SART/WoZ) an ANOVA comparing autistic versusnon-autistic participants utilizing Levene's test showed that thevariance was significant in all scores such that: MaRs-IB scores were(F(1, 129)=4.143, p=0.044), AQ scores were (F(1, 129)=81.090, p<0.001),ASRS-1 scores were (F(1, 129)=4.832, p=0.030), and ASRS-2 scores were(F(1, 129)=8.075, p=0.005).

Similarly, and for the identical sample, an ANOVA comparingparticipants' genders utilizing Levene's test showed that the variancewas insignificant in MaRs-IB scores were (F(1, 129)=0.143, p=0.705), AQscores were (F(1, 129)=0.008, p<0.930), ASRS-1 scores were (F(1,129)=0.973, p=0.326), and ASRS-2 scores were (F(1, 129)=0.018, p=0.893).Cohort and group score averages are listed in Table 9, and shown in FIG.7 :

TABLE 9 Cohort ASC NT MaRs-1B N = 131 N = 71 N = 60 Average  67.006%70.567%  62.792% Maximum 100.000% 100.00%  92.308% Minimum  18.667%18.667%  20.000% AQ (50) N = 131 N = 71 N = 60 Average 25.832 31.36619.283 Maximum 45.000 45.000 41.000 Minimum 3.000 9.000 3.000 ASRS(Everyday Distractibility/Attention) N = 131 N = 71 N = 60 Score A Ave2.954 3.211 2.650 Score B Ave 5.275 5.915 4.517 Score A Max 6.000 6.0006.000 Score A Min 0.000 0.000 0.000 Score B Max 12.000 12.000 11.000Score B Min 0.000 0.000 0.000

SART/WoZ Results/Data Analysis:

Errors of Commission (EOC) Performance

For the entirety of the SART study, and from baseline-to-baselineretest, the performance of the cohort (N=40) consisting of autistic/ASC(N=19) and neurotypical/non-autistic/NT participants (N=21) exhibited animprovement in performance. That is, there was an average reduction inSustained Attention to Response Task (SART) errors equaling 7.46% forall inhibition measures across the entire cohort (FIG. 8A). The samecohort averaged an improvement of 14.50% (again, in error reduction) fora different interval; that is, from the onset of distraction cues toalert intervention. Finally, similar improvements occurred fromdistraction cues to a differing intervention (this time 10.27%) forcombinatorial assistance (e.g., alerts, filters, and guidance; FIG. 8B).Regardless of the intervention, improvements were markedly prevalent forthe entire cohort. Remarkably, and even after interventional cessation,a long-lasting improvement of 17.52% reduction in errors persisted amongthe cohort once the four technological assists were suspended (FIG. 8C).This resulted in a specific and average improvement of 1.45 fewer errorsper participant, regardless of their diagnoses (group membership). Ineach measure, the improvement trend line was well correlated (baselineto baseline, intervention only, and intervention removal).

Errors of Commission Response Times (EOC-RT)

In general, response times increased for the entire cohort whenparticipants experienced exposure to interventional assistance.Regardless of counterbalancing trials and their internal randomization,the cohort's improved accuracy occurred because of increased/slowing RT(e.g., 21.74% increase from baseline to alert intervention). Note that aslowing in RT is actually a desired effect from the intervention, as isexplained in detail below. It is worth mentioning that unlike EOC, therewas insignificant last effect of RT (resulting in 10.69% fasterresponses once interventions ceased).

In comparison, autistic response times were shorter (faster) thanneurotypical controls. This can be due to various factorsdifferentiating neurodiverse responsivity— including, but not limitedto, greater neural processing, differences in genetic makeup affectingsensory reactivity, and superior activity in the visual cortex(Schallmo, M.-P., & Murray, S. (2016). People with Autism May See MotionFaster. 19). For errors of commission, autistic participants experienceda RT increase of 19.39% (i.e., a desired slowing from onset ofdistraction to guidance intervention) while neurotypical counterpartsproduced an undesirable decreased in RT (speeding up) of nearly onepercent (−0.74%) for the same period. Reaction timing's effect onaccuracy saw an improvement of for 8.67% ASC participants and a 1.27%increase for neurotypical (NT) participants. These results are shown inFIGS. 9A to 9C, which are graphical representations of EOC as it relatesto Response Time (RT) of the full cohort of participants in the SART/WoZstudy described herein. FIG. 9A shows the EOC vs RT from startingbaseline to final baseline, FIG. 9B shows the EOC vs RT interventioneffect, and FIG. 9C shows the lasting effect of EOC vs RT.

Note that a slowing of reaction time portends to greater mindfulness,which can be defined as a participant's awareness of their internalfeelings and a subsequent ability to maintain awareness withoutevaluation or judgement (e.g., defined as an outcome). Therapeuticallyspeaking, the wearable device described herein cultivates mindfulnessvis a vis bespoke intervention (assistive technology). This helps toshift and shape a participant's wandering mind and their awareness.Essentially, the participants in this study become more aware,productive, and comfortable through alerts, filters, and guidance whenexposed to sensory interruptions during a Sustained Attention toResponse Task (SART). Over time, participants become more attentive,less sensitive, less anxious, and less fatigued.

Realizing that slowing RT is not an unfavorable outcome, but rather adesirable one, the data suggests that NT participants who previouslyexperience decreasing RTs (speeding up that produce smaller performancegains) can be further improved by utilizing alerts rather than guidance.This results in NTs experiencing a desirable increase in RT (slowingdown). Specifically, and for the period of onset of distraction toalert, both ASC and NT slow their RTs. As a result, EOC improved amongboth autistic and non-autistic participants by 26.01% and 17.59%,respectively. For the same period, ASC and NT participants improvedtheir RTs by 2. 94% and 1.9%, respectively.

While neurotypical gains in accuracy appear small (i.e., from 1.27% to1.9%), this represents a 50% (49.60%) improvement. Thus, slowingresponse times, resulting from custom interventional assists, createbetter performance outcomes. Autistic performance also improved by 200%(e.g., 8.67% to 26.01% accuracy). These results are shown in Tables 10Aand 10B:

TABLE 10A Slower (ASC) vs. Faster (NT) Response Times ffect on AccuracyEOC Response (with guidance Times Accuracy intervention) IncreaseIncrease ASC 19.39% 8.67% NT (−0.74%) 1.27%

TABLE 10B Slower (ASC and NT) Response Times Effect on Accuracy EOCResponse (with alert Times Accuracy intervention) Increase Increase ASC 2.94% 26.01% NT 17.59%   1.9%

The divergence between speeding and slowing RTs (and its effect onexperimental and control groups) is not accidental. Evidence of reverseRT effect on accuracy; that is, faster RT produces greater accuracy, issupported after repeated interventional assists are removed and thenmeasured. The long-lasting effect of fewer errors (e.g., 17.92% and17.09% reductions for ASC and NT, respectively) occurred even whenresponse times lessened (e.g., 21.48% and 2.688% faster RTs for ASC andNT, respectively). These are small, but meaningful reductions amountingto average gains of 19.095 ms for autistic and 2.913 ms for non-autisticparticipants. Still, lasting intensification in performance occurred,despite diminishing response times.

The trend or tendencies of response times provide interestingconsiderations. Specifically, and for the entirety of the seven trials,autistic and non-autistic RTs diverge. Autistic RTs increased from 88.89ms to 69.80 ms for baseline to baseline-retest (a speeding up of 29.78ms over the period). In comparison, neurotypical RTs decreased from82.59 ms to 105.46 ms, or a slowing down of 22.86 ms. This renders a52.64 ms gap between the experimental and control group that ismodulated once interventions are applied.

Explicitly, RTs increase (slow down) 17.72 ms for both ASC (i.e., fromdistraction onset to guidance intervention) and for NT (i.e., by 19.06ms for distraction onset to alert interventions). These represent themaximum increases in RT for both groups and are non-contrasting (i.e.,again, both slow down). Equally significant is RTs lasting effect; thatis, neither autistic nor non-autistic participants benefit from aslowing RT once the intervention is removed. Both ASC and NT groupsspeed up their responses by 19.10 ms and 2.91 ms, respectively (eventhough there is positive lasting performance by way of fewer errors).These results are shown in FIGS. 10A to 10C.

Additionally, as shown in FIGS. 11A to 11C, autistic response times aretypically faster than neurotypical participants for the same tasks andinterventions. Similarly, while reduced errors (improved performance)occurs across both groups, autistic participants exhibit greatervariability in improvement, while neurotypical participants producefewer errors overall. The only exception we see is for combinedinterventions (e.g., alerts, filters, and guidance) where both NT andASC are equivalent a lessoning to 7.4 errors each.

In summary, as a cohort and within subjects/groups, performance increase(e.g., fewer errors) stems from interventional support applied andmeasured from the onset of a sensory distraction to assistivetechnology. A 14.5% improvement for the entire cohort results with alertintervention. Modulating the intervention (i.e., applying filters andguidance to the alerts) results in variable improvement as well. Thecohort improved 10.27% in performance from a combination of theseinterventions. Autistic participants revealed greater performance(26.01% fewer errors) with alert intervention, while non-autisticenjoyed a 5.7% improvement through filter interventions. Lasting effectson performance improvement among the entire cohort (17.52%) andindividual groups (ASC 17.92% and NT 17.09%) continued well afterinterventions were suspended.

Reaction times increase when participants receive assistivetechnologies. By slowing down, participants enable and experiencegreater mindfulness which yields increased performance. From baseline toalert interventions, the cohort averaged a 21.74% slowing in RT and fromthe onset of distraction to alerts, the slowing was 11.35%. Whenremoving interventions of any kind and from baseline tobaseline-retesting, RT sped up (decreased) by 10.69%. From anexperimental to control group comparison, autistic and non-autisticparticipants diverge with RTs decreasing for ASC participants andincreasing for NT subjects. Nonetheless, both groups benefit underinterventional measures with increased performance, while neither groupbenefits from any lasting effect on RTs once assistive technologies areremoved.

Errors of Omission (EOO) Performance:

For the entirety of the SART study, and in addition to studying cohortperformance (N=40, ASC=19, NT=21) on Errors of Commission (e.g., notinhibiting a response when instructed to do so), Errors of Omission werealso analyzed. EOO refer to not responding properly for any stimuluswhen inhibition is not warranted or instructed. For the same testingperiod and from baseline-to-baseline retest, EOO increased 49.16%. Thismeans that there was an average increase in Sustained Attention toResponse Task (SART) measuring, on average, 2.2 errors per participant(FIG. 12A).

While not a desirable result, the cohort averaged an improvement wheninterventions were present. Specifically, and from the onset ofdistraction cues to alert interventions there were 47.60% fewer EOO.Similar improvements occurred from distraction cues to combinatorialinterventions (though this time a smaller improvement of 23.12%), asseen in FIG. 12B.

Remarkably, a long-lasting improvement of 10.10% reduction in EOOpersisted among the cohort once the four technological assists weresuspended (FIG. 11C). This was calculated by measuring the errorpercentage increase from baseline to baseline-retest and thensubtracting the error improvement measured from distraction throughbaseline-retest. For each participant, this corresponded to an averageof 1.86 fewer errors for each, regardless of their diagnoses/groupmembership. In each measure, the improvement trend line was wellcorrelated (baseline to baseline, intervention only, and lastingeffect).

Errors of Omission Response Times (EOO-RT)

In general, response times for the entire cohort increased whenparticipants experienced exposure to interventional assistance, but notfrom baseline to baseline-retest (which remained relatively flat at−0.20%; FIGS. 13A to 13C). Regardless of counterbalancing trials andtheir internal randomization, the cohort's initial reduced andeventually improved EOO accuracy occurred because of increased/slowingRT (e.g., 5.16% increase from Baseline to Filter intervention). Again,this slowing in RT is a desired effect from the intervention, as isexplained earlier in the EOC section. It is worth mentioning that unlikeErrors of Commission, there was insignificant lasting effect of RT(resulting in 3.48% faster responses once interventions ceased).

EOO response times resembled EOC for autistic participants; in that,both were faster than neurotypical controls, due in part to previouslymentioned neuronal processing and responsivity. Thus, autisticparticipants experienced an RT increase of 9.28% (i.e., a desiredslowing from onset of distraction to filters intervention), whileneurotypical counterparts produced an undesirable decrease in RT(speeding up) of nearly one percent (4.44%) for the identicalintervention.

In comparison to EOC RT, neurotypical results are slightly faster(poorer), for e.g., EOC vs. EOO yielded 126.94 ms to 122.05 ms.Considerably more favorable results occurred for ASC participants (e.g.,EOC vs. EOO yielded 91.02 ms to 114.19 ms). Contrastingly, RTs' lastingeffect on performance observed as a reduction (speeding up) for ASC(7.25%) and a relative flattening, albeit a slight reduction (1.2%) forNT participants. These results are shown in FIGS. 14A to 14C.

RTs also effect Errors of Omission, when comparing autistic andnon-autistic groups. There is a lessening of EOO (though these stillproduce inaccuracies) among neurodiverse participants (−15.12%).Similarly, an increase in accuracy (less EOOs) are exhibited amongneurotypical participants. Unsurprisingly, faster RT (4.44% in the caseof NT participants from distraction to filter) did, in fact, create moreerrors (15.09%). As would also be expected, slower RTs among autisticparticipants (9.28%) resulted in fewer EOOs (15.12%). Curiously, bothgroups responded oppositely to similar intervention (by way of RTs), andby equal and opposite magnitudes in accuracy with NTs (not ASCparticipants) experiencing greater errors.

This seem implausible; but, when regarding the entirety of data (i.e.,baseline to baseline-retest) for study participants, the RT and EOOcurves are indeed inversely proportional. Longer RT produces, asexpected, fewer EOO. Less correlated, however, are neurotypical RT.Higher (desirable) NT RTs produce fewer errors (also desired) underfilter intervention. However, greater RTs with guidance produce moreEOOs (undesirable). Thus, and depending upon the group, longer RT have adiminishing return on accuracy. Where Errors of Commission bettercorrelate with response time variance, Errors of Omission do notcorrelate well to RT.

Even though there is an improvement among autistic participants bearinggreater accuracy, this occurs through an unusual lessening of EOOresponse times. Greater accuracy (29.07%) and improvement fromdistraction onset to guidance intervention occurs with less RT slowing.Additional deceleration (9.28%) from distraction to filtering producesmore inaccuracies (15.12%). Non-autistic participants accuracy performsas expected; that is, an increase from −4.4% to −1.39% (e.g., a slowingof RT) produces an 8.5% increase in accuracy (15.09% to 23.59%). Theseresults are shown in Tables 11A and 11B:

TABLE 11A Slower (ASC) vs. Faster (NT) Response Times Effect on AccuracyEOO (with Filter intervention for Response both ASC and Times AccuracyNT) Increase Increase ASC 9.28% (−15.12)% NT (−4.44%) 15.09%

TABLE 11B Slower (ASC and NT) Response Times Effect on Accuracy EOO(with Response Guidance Times Accuracy intervention) Increase IncreaseASC 1.24% 29.07% NT (−1.39)% 23.59%

As presented, the correlation between speeding and slowing RTs (and itseffect on the accuracy of experimental and control groups) is notaccidental for EOC. Contrastingly, there is a divergence in EOO scores.Evidence of reverse RT effect on accuracy does occur; that is, faster RTdon't always produce greater inaccuracy.

In the previous table, autistic response times that increased 9.28%resulted in a negative accuracy increase (e.g., inaccuracy). However, aspeeding up (or reduction of response times to 1.24%) produced greateraccuracy (29.07%). This unexpected autistic divergence is not exhibitedin neurotypical EOO. The increase in speed (−4.44%) produces a loweraccuracy of 15.09% errors, while a slowing to 1.39% (an increase inspeed) produces expected and higher accuracy (23.59%). These results areshown in FIGS. 15A to 15C.

The long-lasting effect of fewer errors is absent (e.g., both ASC and NTsee increased EOC by 51.16% and 29.25%, respectively) while RTaccelerated 7.58 ms for autistic and 1.53 ms for non-autisticparticipants. While increased errors are expected when RT is faster,this is in direct contrast to EOC lasting effect. Put simply, just as RTerrors of omission does correlate well to EOC, the lasting effectexhibited on EOC performance/accuracy does not hold true for EOO.

Like EOC RT, EOO responses for autistic participants response remainboth narrow and consistently faster than their more variable andneurotypical counterparts. And while reduced errors of omission(improved performance) occurs across autistic participants, there isless variability in this improvement, while neurotypical participantsdon't necessarily produce fewer errors. This is in stark contrast to EOCdata. These results are shown in FIGS. 15A to 15C.

In summary, unlike the identical cohort and within subjects/groups thatexperienced a performance increase (e.g., fewer errors of commission),errors of omission were not equally reduced from the onset of a sensorydistraction to assistive technology. While a 15.16% reduction in EOOoccurred for autistic participants (from distraction onset to filterintervention), no reduction occurred for any interventional applicationamong neurotypical participants.

The combined effect on the entire cohort also proved unremarkable froman EOO improvement standpoint. Again, only the autistic (experimental)group experienced benefits. It's worth mentioning that modulating theintervention to other forms (e.g., alters, guidance and combinations)had no appreciable improvement for neurodiverse participants. Onlyfilter intervention proved assistive. Lasting effects of performanceimprovement eschewed the entire cohort as there were 10.10% more errorsof omission. The same remained consistent for experimental and controlgroups once interventions were suspended (e.g., 51.16% and 29.25%increase in EOO for ASC and NT, respectively).

Reaction times increased for the entire cohort when assistivetechnologies were invoked. By slowing down, participants enable andexperience greater mindfulness which yields increased performance. Frombaseline to filter interventions, ASC participants averaged a 9.28%slowing in RT and from the onset of distraction to alerts, the slowingwas 1.535%. Neurotypical participants undesirably sped up 4.44% underthe same filter interventions but managed to slow down for both guidance(2.61%) and combination interventions (4.58%).

When removing entire cohort interventions of any kind and from baselineto baseline-retesting, RT sped up (decreased) by 3.48%. Thus, there wasno significant lasting effect on EOO RT.

Similarly, and for both experimental to control groups, autistic andnon-autistic participants experienced decreasing RTs and no significantlasting effect on EOO. ASC RTs sped up 7.25% whilst NT RTs sped up1.20%.

Tables 12A-12B show some example design specifications, includinglatency parameters, for implementing audiometric sensing,physiological/psychophysiological sensing, and transmission inaccordance with some implementations of the disclosure. It should beappreciated that system specifications can vary depending on theavailable hardware.

TABLE 12A (design specifications for low performance) ProtocolDescription Range Latency Bitrate Audiometric Omnidirectional 50 Hz-20kHz 11.61-23.22 ms 512-1024 samples sensing dynamic or response; −42 to−30 @ 44.1 kHz moving coil dBv sensitivity, S/N 60 sampling ratemicrophone dBA, and 2 KΩ output Physiological/ GSR SCL 2-20 μS; SCR 1-3s; SCR Frequency 1-3 pm Psychophysiological conductance Change in SCL1-3 μS; rise time 1-3 s; sensing and triaxial Amplitude 0.2-1 μS; SCRhalf recovery accelerometer time 2-10 s Bluetooth Headset 5-30 meters200 ms 2.1 Mbps transmission wearable to mobile phone Wireless 32 mindoors ~150 ms 600 Mbps transmission 95 m outdoors

TABLE 12B (design specifications for enhanced performance) ProtocolDescription Range Latency Bitrate Audiometric sensing Omnidirectional 20Hz-20 kHz 2.9-5.8 ms 128-256 dynamic or moving response; −42 dBv samples@ 44.1 coil microphone sensitivity, S/N 39 kHz sampling dBA, and 1 KΩoutput rate Physiological/ GSR conductance SCL 2 μS; SCR 1 s; SCR riseFrequency 3 pm Psychophysiological and triaxial Change in SCL 1 μS; time1 s; SCR half sensing accelerometer Amplitude 0.2 μS; recovery time 2 sBluetooth Headset wearable to 30 meters 200 ms 2.1 Mbps transmissionmobile phone or computer Wireless Mobile or computer 32 m indoors ~150ms 600 Mbps transmission to router 95 m outdoors

Applications

The multi-sensory assistive wearable technology described herein can beutilized across a myriad of applications to supply a myriad of potentialadvantages. For example, in an employment application, the technologydescribed herein can potentially reduce distractibility, improveattention and performance, lower anxiety, and/or increase employeeoutput and/or satisfaction. Metrics that could potentially be improvedin the employment application include improved onboarding and trainingof neurodiverse, autistic, and neurotypical applicants and new hires,reduced employee turnover, increased productivity rate, diversity and/orinclusion, increased profit per employee, lowered healthcare costs,and/or ROI, employee net promoter score, cost of HR per employee,employee referral, combinations thereof and the like.

In an academic application, the technology described herein canpotentially increase concentration and/or comprehension, and reduced,minimized and/or substantially eliminated hesitation and/or increased,enhanced and/or increased comfort. Metrics that could potentially beimproved in an academic application include retention rates (next termpersistence versus resignation), graduation rates, time to completion,credits to degree and/or conferrals, academic performance, educationalgoal tracking, academic reputation, and/or underemployment of recentgraduates.

In a social application, the technology described herein can potentiallyincrease participation and/or motivation, and reduce apprehension.Metrics that could potentially be improved in a social applicationinclude primary socialization (learn attitudes, values, and/or actionsappropriate to individuals and culture), secondary socialization (learnbehavior of smaller groups within society), developmental socialization(learn behavior in social institution and/or developing social skills),anticipatory socialization (rehearse future positions, occupations,and/or relationships), and resocialization (discarding former behaviorand/or accepting new patterns as part of transitioning one's life).

In a transportation lorry/trucking application, the technology describedherein can potentially increase and/or improve attention and/orperformance, reduce fatigue, and improve response times. Metrics thatcould potentially be improved in a transportation lorry/truckingapplication include logistics benefits including increased safety and/orproductivity (shut down engine, recommend rest, crash data statisticsand/or analysis, etc.), reduced logistical strain and/or financialburden (reduced shipping, delivery time, and/or transportation costs),effective planning, dispatch, and/or scheduling.

In a transportation aircraft application, the technology describedherein can potentially increase focus and/or performance, and reducefatigue and/or apprehension reduction. Metrics that could potentially beimproved in a transportation aircraft setting include safety (e.g.,fatality and/or accident rate, system risk events, runway incursions,hazard risk mitigation, commercial space launch incidents, world-widefatalities), efficiency (taxi-in/out time, gate arrival/delay,gate-to-gate times, distance at level-flight descent, flown v. filedflight times, average distance flown, arrival and/or departure delaytotals, number of operations, on-time arrivals, average fuel burned),capacity (average daily capacity and daily operations, runway pavementconditions, NAS reliability), environment (noise exposure, renewable jetfuel, NAW-wide energy efficiency, emission exposure), and/or costeffectiveness (unit per cost operation).

In an IoT application, the technology described herein can potentiallyintegrate mechanical and digital machines, objects, animals, and/orpeople (each with unique identifiers) received transferred informationfrom the wearable so that actionable commands and/or analyses can occur.Metrics that could potentially be improved in the IoT applicationinclude an increase in physiological/psychophysiological activity canprovide alerts to parents, caregivers, and/or professionals (para andotherwise) in the event wearable thresholds are exceeded. Integration toenvironmental control units (ECU) bridge between the wearable andappliances including, but not limited to TV's, radios, lights, VCR's,motorized drapes, and/or motorized hospital beds, heating, and/orventilation units (air-con), clothes washers and/or driers.

In a performance enhancement application, the technology describedherein can potentially improve procrastination, mental health, fatigue,anxiety, and/or focus. Metrics that could potentially be improved in aperformance enhancement application include testing (logic processing,advocacy, curiosity, technical acumen and/or tenacity), Leadership(mentorship, subject matter expertise, team awareness, interpersonalskills, reliability), Strategy & Planning (desire, quality, community,knowledge and functionality), Intangibles (communication, diplomacy,negotiations, self-starter, confidence, maturity and selflessness).

In telemedicine, emergency medicine, and healthcare application, thetechnology described herein can potentially improve the ability formedical and healthcare practitioners to share data with wearable usersto help fine tune therapies, Rx, dispatch for emergency assist, surgicalsuite monitoring and/or optimization, work-schedule, and/or logisticsstrategy, pupillometry indicating unsafe conditions, unsafe warnings ifthresholds are crossed (performance orphysiological/psychophysiological). Metrics that could potentially beimproved in a telemedicine, emergency medicine, and/or healthcareapplication include telemedicine metrics (e.g., consultation time,diagnoses accuracy, rate of readmission, quality of service/technology,patient and/or clinician retention, time and/or travel saved, treatmentplan adherence, patient referral), surgical metrics (e.g., first casestarts, turnover times, location use/time, complications, value-basedpurchasing, consistency of service, outcomes), emergency metrics (e.g.,average patient flow by hour, length of processing/stay,Time-to-Relative Value Unit, Patients Seen, RVU produced, CurrentProcedural Terminology (CPTs) performance, average evaluation andmanagement distribution percentage, total number of deficient charts.

In a parental, guardian, and/or educational monitoring application, thetechnology described herein can potentially abet metric parenting (andguardianship) whereby work-life balance is made possible by meetingactionable and measurable goals and deadlines to improve familydynamics, including being more present, aware, and/or trackingengagement of children (particularly those with exceptionalities,although it is not limited to gifted, neurodiverse but all children).Metrics that could potentially be improved in a parental, guardian,and/or educational monitoring application include family time,engagement, academic improvement, reduction in digital mediatechnologies, screen time, online and console gaming, scheduleadherence, nutritional faithfulness, safety and/or exposure to substanceabuse, seizure and/or location monitoring.

As various changes could be made in the above systems, devices andmethods without departing from the scope of the invention, it isintended that all matter contained in the above description shall beinterpreted as illustrative and not in a limiting sense. Any numbersexpressing quantities of ingredients, constituents, reaction conditions,and so forth used in the specification are to be interpreted asencompassing the exact numerical values identified herein, as well asbeing modified in all instances by the term “about.” Notwithstandingthat the numerical ranges and parameters setting forth, the broad scopeof the subject matter presented herein are approximations, the numericalvalues set forth are indicated as precisely as possible. Any numericalvalue, however, may inherently contain certain errors or inaccuracies asevident from the standard deviation found in their respectivemeasurement techniques. None of the features recited herein should beinterpreted as invoking 35 U.S.C. § 112, paragraph 6, unless the term“means” is explicitly used.

In this document, the terms “machine readable medium,” “computerreadable medium,” and similar terms are used to generally refer tonon-transitory mediums, volatile or non-volatile, that store data and/orinstructions that cause a machine to operate in a specific fashion.Common forms of machine-readable media include, for example, a harddisk, solid state drive, magnetic tape, or any other magnetic datastorage medium, an optical disc or any other optical data storagemedium, any physical medium with patterns of holes, a RAM, a PROM,EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, andnetworked versions of the same.

These and other various forms of computer readable media can be involvedin carrying one or more sequences of one or more instructions to aprocessing device for execution. Such instructions embodied on themedium, are generally referred to as “instructions” or “code.”Instructions can be grouped in the form of computer programs or othergroupings. When executed, such instructions can enable a processingdevice to perform features or functions of the present application asdiscussed herein.

In this document, a “processing device” can be implemented as a singleprocessor that performs processing operations or a combination ofspecialized and/or general-purpose processors that perform processingoperations. A processing device can include a CPU, GPU, APU, DSP, FPGA,ASIC, SOC, and/or other processing circuitry.

The various embodiments set forth herein are described in terms ofexemplary block diagrams, flow charts and other illustrations. As willbecome apparent to one of ordinary skill in the art after reading thisdocument, the illustrated embodiments and their various alternatives canbe implemented without confinement to the illustrated examples. Forexample, block diagrams and their accompanying description should not beconstrued as mandating a particular architecture or configuration.

Each of the processes, methods, and algorithms described in thepreceding sections can be embodied in, and fully or partially automatedby, instructions executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmscan be implemented partially or wholly in application-specificcircuitry. The various features and processes described above can beused independently of one another, or can be combined in various ways.Different combinations and sub-combinations are intended to fall withinthe scope of this disclosure, and certain method or process blocks canbe omitted in some implementations. Additionally, unless the contextdictates otherwise, the methods and processes described herein are alsonot limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate, or can be performed in parallel, or in some other manner.Blocks or states may be added to or removed from the disclosed exampleembodiments. The performance of certain of the operations or processescan be distributed among computer systems or computers processors, notonly residing within a single machine, but deployed across a number ofmachines.

As used herein, the term “or” can be construed in either an inclusive orexclusive sense. Moreover, the description of resources, operations, orstructures in the singular shall not be read to exclude the plural.Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. Adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known,” and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass conventional, traditional, normal, or standard technologiesthat may be available or known now or at any time in the future. Thepresence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent.

We claim:
 1. A system, comprising: a wearable device comprising one ormore sensors; one or more processors; and one or more non-transitorycomputer-readable media having executable instructions stored thereonthat, when executed by the one or more processors, cause the system toperform operations comprising: connecting to a datastore that stores oneor more sensory thresholds specific to a user of the wearable device,the one or more sensory thresholds selected from auditory, visual orphysiological sensory thresholds; recording, using the one or moresensors, a sensory input stimulus to the user; comparing the sensoryinput stimulus with the one or more sensory thresholds specific to theuser to determine an intervention to be provided to the user, theintervention configured to provide the user relief from distractibility,inattention, anxiety, fatigue, or sensory issues; and providing theintervention to the user, the intervention comprising filtering, inreal-time, an audio signal presented to the user or an optical signalpresented to the user.
 2. The system of claim 1, wherein: the operationsfurther comprise: communicatively coupling the system to an Internet ofThings (IoT) device, the sensory input stimulus generated at least inpart due to sound emitted by a speaker of the IoT device or lightemitted by a light emitting device of the IoT device; and providing theintervention to the user, comprises: controlling the IoT device tofilter, in real-time, the audio signal or the optical signal.
 3. Thesystem of claim 2, wherein: the IoT device comprises the light emittingdevice; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.
 4. The system of claim 2, wherein: the IoT device comprises thespeaker; controlling the IoT device to filter, in real-time, the audiosignal or the optical signal, comprises controlling the IoT device tofilter, in real-time, the audio signal; and filtering the audio signaladjusts a frequency of sound output by the speaker.
 5. The system ofclaim 1, wherein: the wearable device further comprises a boneconduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.
 6. Thesystem of claim 5, wherein: the wearable device further comprises a headmounted display (HMD) that presents the optical signal to the user, theHMD worn by the user; and providing the intervention to the user furthercomprises filtering, in real-time, the optical signal by modifying areal-time image of the real-world environment presented to the user viathe HMD.
 7. The system of claim 6, wherein comparing the sensory inputstimulus with the one or more sensory thresholds specific to the user todetermine the intervention to be provided to the user, comprises:determining, based on the same sensor data recorded by the one or moresensors, to filter the audio signal and to filter the optical signal. 8.The system of claim 1, wherein: the wearable device further comprises aHMD that presents the optical signal to the user, the HMD worn by theuser; and providing the intervention to the user includes filtering, inreal-time, the optical signal by modifying a real-time image of thereal-world environment presented to the user via the HMD.
 9. The systemof claim 8, wherein modifying the real-time image comprises inserting avirtual object into the real-time image or modifying the appearance ofan object of the real-world environment in the real-time image.
 10. Thesystem of claim 8, wherein comparing the sensory input stimulus with theone or more sensory thresholds specific to the user to determine theintervention to be provided to the user, comprises: inputting thesensory input stimulus and the one or more user-specific sensorythresholds into a trained model to automatically determine, based on anoutput of the trained model, a visual intervention to be provided to theuser.
 11. The system of claim 10, wherein: the one or more sensorscomprise multiple sensors of different types, the multiple sensorscomprising: an auditory sensor, a galvanic skin sensor, a pupillarysensor, a body temperature sensor, a head sway sensor, or an inertialmovement unit; recording the sensory input stimulus to the usercomprises recording a first sensory input stimulus from a first sensorof the multiple sensors, and a second sensory input stimulus from asecond sensor of the multiple sensors; and inputting the sensory inputstimulus into the trained model comprises inputting the first sensoryinput stimulus and the second sensory input stimulus into the trainedmodel.
 12. The system of claim 8, wherein the visual interventioncomprises: presenting an alert to the user of a visually distractingobject; and after it is determined that the user does not sufficientlyrespond to the alert within a period of time, filtering, in real-time,the optical signal presented to the user.
 13. The system of claim 8,wherein the visual intervention comprises: filtering, in real-time, theoptical signal to hide a visually distracting object without providing aprior alert to the user that the visually distracting object is present.14. The non-transitory computer-readable medium of claim 1, wherein theoperations further comprise determining the one or more sensorythresholds specific to the user and one or more interventions specificto the user by: presenting multiple selectable templates to the user,each of the templates providing an indication of whether the user isvisually sensitive, sonically sensitive, or interoceptively sensitive,and each of the templates associated with corresponding one or moresensory thresholds and one or more interventions; and receiving datacorresponding to input by the user selecting one of the templates. 15.The non-transitory computer-readable medium of claim 14, whereindetermining the one or more sensory thresholds specific to the user andthe one or more interventions specific to the user further comprises:receiving additional data corresponding to additional user inputselecting preferences, the preferences comprising audio preferences,visual preferences, physiological preferences, alert preferences,guidance preferences, or intervention preferences; and in response toreceiving the additional data, modifying the one or more thresholds andthe one or more interventions of the selected template to derive the oneor more sensory thresholds specific to the user and the one or moreinterventions specific to the user.
 16. The non-transitorycomputer-readable medium of claim 1, wherein comparing the sensory inputstimulus with the one or more sensory thresholds specific to the user todetermine the intervention to be provided to the user, comprises:inputting the sensory input stimulus and the one or more user-specificsensory thresholds into a trained model to automatically determine,based on an output of the trained model, the intervention to be providedto the user.
 17. The system of claim 1, wherein the user isneurodiverse.
 18. The system of claim 17, wherein user is autistic. 19.The system of claim 18, wherein: the intervention further comprises analert intervention; and with the alert intervention, a response time forthe user increases by at least 3% and accuracy increases by at leastabout 26% from baseline for errors of commission, the errors ofcommission being a measure of a failure of the user to inhibit aresponse when prompted by a feedback device.
 20. The system of claim 18,wherein: the intervention further comprises a guidance intervention; andwith the guidance intervention, a response time for the user increasesby at least about 20% and accuracy increases by at least about 10% frombaseline for errors of commission, the errors of commission being ameasure of a failure of the user to inhibit a response when prompted bya feedback device.
 21. The system of claim 18, wherein: the interventionfurther comprises a guidance intervention; and with the guidanceintervention, a response time for the user increases by at least about2% and accuracy increases by at least about 30% from baseline for errorsof omission, the errors of omission being a measure of a failure of theuser to take appropriate action when a prompt is not received from afeedback device.
 22. The system of claim 18, wherein: with theintervention to filter, a response time for the user increases by atleast about 10% from baseline for errors of omission, the errors ofomission being a measure of a failure of the user to take appropriateaction when a prompt is not received from a feedback device.
 23. Thesystem of claim 18, wherein: with the intervention to filter, a responsetime for the user is at least about 15% faster than would be a responsetime for a neurotypical user using the system for errors of omission,the errors of omission being a measure of a failure of the user to takeappropriate action when a prompt is not received from a feedback device.24. The system of claim 18, wherein: the intervention further comprisesa guidance intervention; and with the guidance intervention, a responsetime for the user is at least about 20% faster and accuracy is about 8%higher than would be a response time and accuracy of a neurotypical userusing the system for errors of commission, the errors of commissionbeing a measure of a failure of the user to inhibit a response whenprompted by a feedback device.
 25. The system of claim 18, wherein: theintervention further comprises an alert intervention; and with the alertintervention, accuracy for the user is at least about 25% higher thanwould be an accuracy of a neurotypical user using the system for errorsof commission, the errors of commission being a measure of a failure ofthe user to inhibit a response when prompted by a feedback device.
 26. Amethod, comprising: connecting a wearable device system to a datastorethat stores one or more sensory thresholds specific to a user of awearable device of the wearable device system, the one or more sensorythresholds selected from auditory, visual or physiological sensorythresholds; recording, using one or more sensors of the wearable device,a sensory input stimulus to the user; comparing, using the wearabledevice system, the sensory input stimulus with the one or more sensorythresholds specific to the user to determine an intervention to beprovided to the user, the intervention configured to provide the userrelief from distractibility, inattention, anxiety, fatigue, or sensoryissues; and providing, using the wearable device system, theintervention to the user, the intervention comprising filtering, inreal-time, an audio signal presented to the user or an optical signalpresented to the user.
 27. The method of claim 26, wherein: the methodfurther comprises communicatively coupling the wearable device system toan Internet of Things (IoT) device; providing the intervention to theuser comprises controlling the IoT device to filter, in real-time, theaudio signal or the optical signal; and the sensory input stimulusgenerated at least in part due to sound emitted by a speaker of the IoTdevice or light emitted by a light emitting device of the IoT device.28. The method of claim 27, wherein: the IoT device comprises the lightemitting device; controlling the IoT device to filter, in real-time, theaudio signal or the optical signal, comprises controlling the IoT deviceto filter, in real-time, the optical signal; and filtering the opticalsignal adjusts a brightness or color of light output by the lightingdevice.
 29. The method of claim 27, wherein: the IoT device comprisesthe speaker; controlling the IoT device to filter, in real-time, theaudio signal or the optical signal, comprises controlling the IoT deviceto filter, in real-time, the audio signal; and filtering the audiosignal adjusts a frequency of sound output by the speaker.
 30. Themethod of claim 26, wherein: the wearable device further comprises abone conduction transducer or a hearing device; and providing theintervention to the user comprises: filtering, at the wearable device,in real-time, the audio signal in a frequency domain; and afterfiltering the audio signal, presenting the audio signal to the user byoutputting, using the bone conduction transducer or the hearing device,a vibration or sound wave corresponding to the audio signal.